Do people only believe what they want to believe? An empirical analysis of the Pygmalion effect in telemedicine platforms based on linear regression algorithms

Do people only believe what they want to believe? 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An empirical analysis of the Pygmalion effect in telemedicine platforms based on linear regression algorithms Xin Shen, Yulin Yan, Huikang Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4449255/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract In addition to exploring how people's expectations and beliefs about healthcare information and services affect their actual health outcomes, this study aims to empirically analyze whether there is a Pygmalion effect in healthcare platforms using machine learning and natural language processing. Regression modeling will be used to provide treatment recommendations for more common diseases. We gathered a 10-month panel dataset from a major Chinese online medical portal, containing information from 10,243 physicians. We discovered a strong linear correlation between users' expectations for their final level of recovery and satisfaction and their access to doctors, medical information, treatment alternatives, and healthcare experiences. People's choice of therapy for more complicated illnesses, like heart valve lesions and breast cancer, should lean more away from conventional information sources. Patients' expectations and treatment adherence are strongly connected with the expectations of their doctors, and treatment outcomes are also significantly influenced by the beliefs and expectations of the patients themselves. Using sentiment analysis and multiple robustness polls of user ratings on healthcare platforms, we demonstrate that the treatment choices made by users are distributed linearly across various complexity levels of diseases. As a result, this study highlights the real influence of patient and physician expectations and beliefs on healthcare outcomes, proves the presence of the Pygmalion effect on healthcare platforms, and explores it for particular diseases. This has real-world implications for raising patient happiness, enhancing medical service quality, and strengthening the doctor-patient bond. Pygmalion effect interaction machine learning online health community multiple linear regression (MLR) emotional analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction A researcher has done such an experiment: $ 10,000 worth of red wine labeled with $ 500, and at the same time to $ 10,000 red wine labeled with $ 500, to the test persons to taste and ask them to evaluate, the results of the results through the aggregation of more than 95% of the participants think that $ 10,000 labeled red wine tastes more appealing to them. Despite the randomized nature of the experiment, it still illustrates the Pygmalion effect, a theme that this paper seeks to explore. With the development of the web3, the use of third-party information platforms to integrate information from multiple websites at the same time is increasingly appealing to people with different needs. Some of the more established applications include the online healthcare platforms explored in detail in today's paper [ 28 , 45 ].It is widely known that medical decision-making, despite being as humanized and precise as possible nowadays, also still suffers from decision-making bias caused by a variety of reasons, and that medical decision-making is a cumbersome process influenced by the complex relationships between patients, doctors, and healthcare professionals. Into this delicate balance creeps the Pygmalion effect, described in psychology, which injects a dash of complexity into decision-making. This effect reveals that when faced with conflicting choices or information, people tend to choose the option that is more familiar or consistent with their existing beliefs. In healthcare, this phenomenon may have far-reaching implications for the decision-making process of both patients and physicians. More obvious and easy to study are online healthcare platforms such as webmd, practo, good doctors etc. Enabling physicians to fully utilize the use of their spare time and medical expertise to serve remote patients enables patients to access online healthcare services through online platforms[ 23 , 49 , 37 ]. People can access health information and services or manage their health status by consulting with remote physicians in hospitals across the country[ 40 ]. The recent changes in healthcare choices in online health communities are all a widespread and ongoing phenomenon, as patients have limited receptivity to reliable information, and patients are more likely to be biased due to their lack of specialized medical knowledge. One of the most common scenarios is that patients tend to be more inclined to choose doctors who match their pre-existing beliefs or expectations, as well as epitomizing the choice of treatment options, and doctor-patient communication. This is a more common manifestation of the Pygmalion effect. On online healthcare platforms, physicians can present their medical knowledge and treatment information to patients who visit the online platform [ 30 ]. At the same time, communication between doctors and patients is posted on the healthcare platform network. There is a wealth of valuable information that can help build knowledge-based online support for patients [ 13 ]. Therefore, it is necessary for patients to build mental anticipation and preparedness to recognize this effect when reviewing the large amount of health information displayed to understand the disease. This not only has important implications for patient safety, but is also a determinant of the efficient and rational use of healthcare resouces [ 7 ]. It also has greater significance in assessing the quality of service and electronic word-of-mouth (eWOM) of physicians. This study contributes to the research community in several ways. First, this study is one of the first to investigate the Matthew effect in the online healthcare community by investigating the impact of online ratings on physician platform revenue. While previous studies have investigated the relationship between online ratings and product sales and seller revenues in the retail industry [ 8 ], the findings may not be applicable to services in the health industry. Health services, in which there are inherent differences and class subjectivity in service experiences, service content and quality are even more directly related to patient health. Unlike retail operations, little is known about the Marion impact of online ratings on health care. Therefore, this study explores this gap from the perspective of the Marion effect. Second, we provide insight into the relationship between the Marion effect, treatment resource selection, and physician diagnostic quality by examining and validating the moderating role of physician attributes through a sequence of hypotheses followed by arguments. Recognizing the presence of the Marion effect in medical decision-making helps healthcare professionals to assess the patient's situation more comprehensively and provide more balanced and objective information to facilitate more rational and integrated medical decision-making. At the same time, patients can be more open to different perspectives and treatment options for better medical outcomes, providing a clearer understanding of the intersection of psychology and medicine and enhancing communication with physicians. The remainder of this study is as follows: in Section 2 , we review the literature in more details. In Section 3 , we begin by preparing to accept the development of the hypotheses. Section 4 describes the research methodology. Section 5 discusses the key findings, theoretical implications, practical implications and limitations of the study. Finally, in Section 6 , we summarize the results of this study. 2. literature review 2.1 Related research The Pygmalion effect refers to a psychological phenomenon in which beliefs or expectations that are expected to lead to the occurrence of an outcome make that outcome more likely to occur .In a healthcare setting, the attitudes, words, or actions of doctors, nurses, or other healthcare professionals toward patients or the care they provide may affect patients' expectations, which in turn may affect treatment outcomes or the recovery process.The Pygmalion effect has attracted a great deal of research effort, part of which seeks to gain insights into the interactions between patients, physicians, and the healthcare environment in order to optimize medical practice and improve patient outcomes. For example, patients with suspected COVID − 19 tend to exhibit symptoms of depression, anxiety, and irritability during quarantine, and psychological care based on the Pygmalion effect can help alleviate their negative emotions [ 47 ].In examining the Pygmalion effect in nursing home caregiver expectations on patient prognosis [ 27 ].The application of the Pygmalion effect intervention model to elderly patients with COPD and lung infections promotes clinical regression, improves their pulmonary function and quality of life, and thus enhances satisfaction with care[ 26 ]It is believed that the Pygmalion effect, when applied to breast cancer patients during surgery, can improve their psychological resilience, enhance their belief in healing, effectively improve their treatment compliance, and J.Y.Kim et al.lead to a rapid recovery from the disease, which is worthy of popularization and application [ 24 ]. A systematic evaluation revealed different domains of doctor-patient relationship and communication with convincing effects on different objective and subjective health outcomes [ 38 ]. It is believed that the Pygmalion effect intervention is effective in improving respiratory exercise adherence and lung function levels in patients undergoing surgery for esophageal cancer, enhancing psychological resilience and self-efficacy, and predisposing them to face and deal with problems in a positive attitude and manner [ 20 ]. In online healthcare, there is an important influence of the Pygmalion effect. For example, a physician's or nurse's cordial attitude, professional explanations, or advice provided to a patient during a teleconsultation may enhance the patient's confidence in treatment, thereby promoting better adherence to medical advice and improving treatment outcomes [ 1 ]. In addition, the design and functionality of online healthcare platforms may also influence patients' expectations, e.g. user-friendly interfaces and operational processes may enhance patients' confidence in healthcare services, thereby improving treatment outcomes. More obvious and easy to research are online healthcare platforms such as webmd, practo, good doctors, etc. These platforms allow doctors to make the best use of utilizing their spare time and medical expertise to provide services to remote patients, enabling patients to experience appropriate services through online platforms [ 11 ]. People can access health information and services or manage their health conditions by consulting remote doctors in hospitals across the country [ 15 ]. On online healthcare platforms, doctors can present their medical knowledge and treatment information to patients who visit the online platform. At the same time, the communication between doctors and patients also posts that there is a large amount of valuable information on the network of healthcare platforms that can help build knowledge-based online support for patients [ 29 ]. This paper focuses on how people's expectations and beliefs about healthcare information and services affect their actual health outcomes. The most relevant literature to this study is the empirical analysis of the Matthew effect on online healthcare communities [ 50 ]. For example, Zhuo et al. explains that online physician ratings from third-party platforms are important for patient decisions in this particular healthcare context. Therefore, understanding and capitalizing on the Pygmalion effect is important in online healthcare to enhance patient confidence and treatment outcomes through the provision of high-quality healthcare services and the design of user-friendly platforms. This study reveals the existence of the Pygmalion effect on healthcare platforms, emphasizing the actual impact of expectations and beliefs between physicians and patients on healthcare outcomes. 2.2. Online service and interactions in medical information Online service has been widely studied as a new type of information access means, and in the access to medical information, online platforms provide more channels and ways [ 46 ]. This is also an expression of interactions, and we have done a research on the common behaviors when accessing medical information. People often ask people they know around them, which is an extremely common and cheap way to obtain information, but people, as emotional animals, often filter the information and add a certain subjective color to express it to the other side, which leads to the information obtained through this channel is not widely applicable and a certain degree of deception. The Marion effect is manifested in the fact that information obtained from people who know the person well can always bring great psychological comfort, especially to those who are ill, but this information also influences the patient's rational judgment to some extent, thus delaying the illness [ 2 ]. This information asymmetry and the main theoretical framework, interaction theory, which will be presented below, are the underlying logical foundations that lead to the Pygmalion effect. Search engine queries e.g. through search engines such as Google, Baidu, etc., typing in symptoms, disease names or other relevant medical keywords in order to obtain relevant information is also a favored way [ 28 ]. And as mentioned above, the maturity of web 2.0 has also brought medical website browsing: visiting professional medical websites and health information platforms, such as WebMD, Mayo Clinic, etc., for medical advice and professional opinions into the public eye [ 42 ]. This also includes the platforms that will be the focus of this paper's investigation. Second, social media participation allows for the sharing of personal experiences, reading health blogs and articles provides access to professional information on health and medical care, medical literature searches provide access to more in-depth medical knowledge and the latest research findings, and mobile app use and participation in online Q&A platforms allow for a more personalized and self-directed treatment process [ 33 ]. All of these behaviors reflect the diverse access to healthcare information in modern society, leading to a more comprehensive understanding and management of one's health [ 4 ]. However, it is important to note that this process is influenced by the psychological effects of the individual patient as well as the environment. Individual psychological processes are subject to two types of social influences: normative influences and informational influences [ 9 ]. Normative Influence Under affective influence, individuals process information and make decisions based on heuristic cues Informational Influence, individuals expend cognitive effort to process information and make decisions [ 16 ]. As a result, normative affect requires less cognitive effort from the individual than it does from the individual's perceived information relationships [ 21 ]. There is then 1. Information Overload: The internet provides a large amount of medical information, which can lead to patients feeling overwhelmed. Information overload may make it difficult to sift through and make sense of information, thus affecting the quality of decision-making.2 Confirmation Bias: People tend to be more receptive to information that is consistent with their existing beliefs or opinions, while ignoring or being skeptical of information that contradicts them. This may lead patients to selectively accept medical information that meets their expectations. 3. Social Comparison: People may compare their medical experiences with those of others, which may trigger anxiety or expectations. The social comparison effect may cause patients to feel uneasy about their health status or to have high expectations of particular treatments [ 36 ]. Unfortunately, these are just three representative psychological effects, along with the physiologic support for the Pygmalion effect in this context. A similar psychological effect related to the Marion effect is the Illusory Correlation effect, in which people may sometimes be inclined to establish false correlations between different pieces of medical information rather than assessing the credibility of the information based on scientific evidence. This can lead to an inaccurate understanding of the information, and in fact, this can be said to have quite a bit in common with the Marion effect, both of which are flaws in information selection due to mental bias. 2.3 Theoretical framework In the context of online health communities, when patients make decisions based on the influence of information (consulting a doctor), they need to spend a lot of effort on information. In the case of online healthcare platforms, for example, information is provided by the website, such as the doctor's personal characteristics or the doctor's past consultation records [ 37 ]. When patients make decisions based on the corresponding influences, they only need heuristic cues, such as the doctor's ratings, honorary titles, and reviews from cured people. Once repeated contact with familiar sources has been made, interactions manifest themselves. Interaction theory, which is widely used and embodied across multiple schools of thought, can find a range of related theories and ideas behind it: one of them, social cognitive theory, will brilliantly help us understand the Pygmalion effect in medical effects. Patients are more likely to adopt trusted, familiar sources of information, and communication between family and friends as well as title-rich specialists can have a big cognitive change on them, even if it may not be the optimal treatment option. According to interaction theory, people's attitudes and preferences are formed through interactions between individuals and external stimuli. This interaction includes repeated exposure of people to something, as well as cognitive and emotional processing within the individual. The Marion effect can be viewed as an interaction between an external stimulus (exposure to something) and an individual's internal mental processes (emotional or cognitive processing). In the Marion effect, repeated exposure to something increases familiarity with it, which in turn decreases its unfamiliarity. This reduced unfamiliarity triggers a more positive emotional response. In other words, when we are repeatedly exposed to something, the brain is more likely to process the information because they become more familiar, which causes us to feel more positive emotions towards them [ 41 ]. From an interaction perspective, this increased familiarity may affect the way an individual cognitively processes that thing, such as recognizing it more quickly, evaluating it more positively, or reacting to it more happily. This change in cognitive processing may lead to a more positive affective experience, which in turn enhances favorable feelings towards the thing. Thus, the Marion effect can be viewed as an interaction between an external stimulus (exposure to something) and an individual's internal affective or cognitive processing, leading to more positive attitudes and preferences towards things. In a more subdivided direction - social cognitive theory - people's behavior and attitudes are influenced by their observations and evaluations of the behavior and attitudes of others [ 19 ]. The Pygmalion effect will not only influence the patient's choices, but will likewise have a change in the physician's choice and evaluation of the platform, thus influencing the patient's choice and evaluation of the treatment platform [ 35 ]. 3. Theoretical background and hypothesis development 3.1 Efficacy modeling Based on previous discussions, this study proposes the research model shown in Figure.1. As the interaction theory and the social cognitive theory under it are the theoretical basis of this paper to study the Marion effect, the theory emphasizes that people interact and influence each other with their surroundings thus forming qualitative changes. The two most important factors as independent variables are separated separately for Fig. 1 , namely 'individual' and 'culture and environment', which as parts constitute the key variables of the interaction theory, and at the same time as a whole for the Pygmalion effect to be Influence. This allows for the following precise conceptualization and definition of the effect. The Pygmalion effect, also known as the self-fulfilling prophecy, is a psychological phenomenon in which the expectations, beliefs, or anticipations of others have an impact on an individual's behavior and performance. In psychology, the Pygmalion effect describes how people tend to conform to what others expect of them [ 14 ]. If a person is told or believes that they have a certain ability, trait, or potential, they are more likely to exhibit behavior that conforms to that expectation. This expectation may be communicated non-verbally or verbally and can come from teachers, parents, employers or other authority figures. The central idea of this effect is that the beliefs or expectations of others may influence individuals' self-perceptions and behaviors, thereby prompting them to exhibit traits or behaviors consistent with those expectations. This phenomenon has significant applications and implications in a variety of fields, including education, workplace environments, leadership, healthcare, and interpersonal relationships. Some of the important components are as follows. 1. Expectations and Expectations: the expectations and anticipations that others have of a person or group. This may be based on beliefs about some specific traits, abilities, or behaviors, usually expressed nonverbally or verbally.2. Self-fulfilling prophecies: People's behaviors and performances may be influenced by the expectations others have of them, which can lead to those expectations becoming reality. If people are told they have a certain ability or trait, they are more likely to demonstrate it.3. Social interaction and communication: This phenomenon is usually realized through social interaction, nonverbal and verbal communication. The expectations of others may be conveyed to the individual through body language, verbal expression, or other means of communication, thereby influencing his or her behavior and performance.4. Individual Performance and Behavior: People may be inclined to conform to the expectations that others have of them. They are more likely to exhibit positive behaviors or abilities if they perceive positive expectations from others. The theoretical basis for this effect lies in the emphasis on the influence of social expectations on individuals, which can change their behavior and performance, making expectations a self-fulfilling prophecy. This concept has important applications for areas such as education, leadership, and interpersonal relationships. 3.2 Decision quality modeling The most important part of the healthcare process, whether online or offline, is the choice of treatment, and due to the intervention of the Marion effect, patients will be more inclined to choose doctors who match their pre-existing beliefs or expectations in this important process. It is based on past experience, tradition, or a match with the personal traits of the doctor. When patients feel that a physician matches their expectations, they are more likely to build trust and satisfaction with the physician [ 10 ]. Similarly, when physicians are making decisions, this effect may lead them to make different medical decisions in similar cases, which will increase the risk for patients. In addition to this, the effect on the physicians themselves should not be underestimated and will be reflected in, for example, rankings, scoring, and follow-up of patients, among other things. Decision quality is often viewed as how close the decision maker's implementation effect is compared to the performance of the actual goal [ 5 ]. It may affect the satisfaction of the initiator of the problem (here the patient, later replaced by patient) and further influence the patient's behavioral intentions such as acceptance and rejection [ 39 ]. For the patient's satisfaction with the treatment decisions proposed by the physician here, we refer to this effect as service quality, which is generally designated as a multidimensional and hierarchical concept [ 25 ]. Many researchers like Gummerus et.al have focused on the measurement of service quality. [ 18 ] Thay used a two-dimensional space (technical quality and functional quality), where technical quality relates to the information that the patient receives from the physician, while functional quality relates to the way in which decisions are provided. [ 31 ] constructed service models that measure patient perceptions of treatment decisions. Regarding healthcare services, [ 12 ] Donabedian et al. used both technical quality and interpersonal quality to measure the quality of healthcare services from different perspectives. The former quality of service refers to the application of medical science and technology in healthcare, while the latter refers to the communication between physicians and patients. McCulloch et al.[ 34 ] also summarized the conclusion - there are three dimensions of quality (system quality, information quality and interaction quality) in healthcare services. so it is not difficult to observe that a good experience of interacting with a doctor can add value in both traditional offline and online healthcare services, but this behavior is greatly affected by the Marion effect. In general, the perceived quality of information affects the patient's diagnostic intent. Other patient-rated service quality influences patient decisions [ 17 ].Patients can evaluate the caliber of a doctor's care with the aid of the wealth of online healthcare data found on online healthcare platforms, including online patient numbers, online reviews, and online medical records. Patients will use this information to help them make an ultimate decision on which doctor to see and how cooperative they are during the course of therapy. Our study will use text mining and sentiment analysis in natural language processing to replicate the above procedure based on prior research; the examination of these variables will be covered in a later section. 3.3 Research hypotheses In this section we continue to use the research model in Fig. 1 to formulate our hypotheses. First, we investigated the main influence of effect on patient's choice of doctor and treatment outcome. In order to rationalize this relationship, we considered some important factors that can moderate the effect of patient's choice of doctor. More specifically, this study investigated the following factors: (1) treatment expectations from the patient, (2) the patient's sources of information, (3) the physician's choice of treatment modality, (4) the patient's affective bias, and (5) is the level of the physician's academic title. Investigating these moderators will help reflect the Pygmalion effect of physicians and online healthcare platforms, which is often based on physician characteristics. A comprehensive understanding of how these moderators influence patients' choices of physicians is important for these platforms to accurately estimate treatment outcomes and provides better guidance for designing appropriate online search matching strategies. Existing literature has long documented online ratings of products or services as key heuristic cues in the consumer decision support process. In the context of the online healthcare industry, upon learning that they have been diagnosed with a particular disease, most patients will be the first to inform their family members, relatives, or even friends for advice and assistance. On online healthcare platforms, patients and their relatives can easily access many relevant medical information, such as relevant patient ratings, review testimonials, and doctor specialization modules. The content showing this doctor will be on the personal page viewed by the patient, so that potential patients can learn about the doctor. Thus, both of these approaches are considered as patients' access to medical information. When patients or their family members perform a search, the portal will always recommend the higher rated doctors first for doctors based on vague conditions related to diseases in the online health community. On the one hand, sorting doctors' ratings in descending order can help patients quickly choose the right doctor because these highly rated doctors tend to be leaders in disease-specific related fields. However, this sorting method greatly reduces the likelihood that patients will consult highly rated and other rated physicians, and the positive bias in patients' ratings (patients' tendency to choose physicians corresponding to positive sources of information) may increase the ratings of these physicians thereby exacerbating this effect. Therefore, we hypothesize that when choosing a physician, patients are more likely to accept a physician who matches the information they themselves have acquired and ignore or reject information that contradicts it. So it also is to an overall extent, this effect exacerbates the vicious circle. It is therefore reasonable to hypothesize that patients are more inclined to trust sources of information with a high degree of cordiality, and that when such information is received to a certain extent, the more likely it is that patients will make choices and judgments that are broadly consistent with it. That is, the Pygmalion effect will continue to operate when more belief-consistent information is available. Based on the above discussion, we expect that the number of sources of information that patients trust and the quality of such information will positively modulate the strength of the Pygmalion effect. Patients' judgments and decisions will be altered as a result. Based on the above discussion, we formulate the following hypotheses: H1. Patients are more likely to receive medical information that is consistent with existing beliefs or expectations, especially in the case of serious illnesses, and the quality and quantity of such information will influence the strength of the Pygmalion effect. Of course, after obtaining information patients will mentally generate a threshold of psychological expectations for this information, and when enduring psychological emotions higher than this value, patients will maintain a consistently positive therapeutic attitude and psychology; conversely, below this level patients will be in the midst of negative psychological cues for a long period of time. Discussing the presence of the former situation, patients have positive expectations of the doctor, they are more likely to actively cooperate with the treatment, which in turn has a better impetus to the results. Similarly, the patient's state of mind is altered from time to time by the interactions of the surrounding environment (Interaction Theory) during the process, which will cause unpredictable fluctuations in treatment outcomes. This is also a good evidence of the direct effect of the Pygmalion effect. Inspired by this, we have also proposed the resonance effect, which, although already in place, is equally applicable in the medical field, where resonating with a treatment program is more likely to have a positive impact on treatment outcomes. Similar to the generation of empathy, when a physician expresses positive expectations for a patient, the patient may be more inclined to trust this physician and to choose treatments that are consistent with the physician's expectations. Responding to the physician's expectations in this way is due to the fact that patients tend to seek guidance in the physician's expertise and advice and want a treatment plan that the physician endorses. This contributes to a positive patient-physician relationship. The patient may feel concerned and supported by the physician and thus be more motivated to actively participate in the treatment process. These are two aspects of the principle of consistency, on which we can expand to get: Consistency of Information Sources: Patients may be influenced by multiple sources of information when making healthcare choices, such as healthcare professionals, family members, friends, the media, or the Internet. If these sources of information consistently emphasize the strengths or effectiveness of a particular doctor or treatment modality, the patient is more likely to choose an option that is consistent with this consistency. Social Expectations and Choice Consistency: Social expectations and anticipation of medical choices may also influence patient decision-making. If society generally agrees that a certain medical option is better, patients may be more likely to choose an option that is consistent with this social expectation in order to avoid social pressure or to gain social acceptance. Due to the Pygmalion effect, the consistency of the patient's choice of treatment may improve treatment outcomes to some extent. Positive attitudes and feelings of trust in patients may influence physiological and psychological processes and have a positive impact on recovery. Therefore, we formulate the following hypotheses: H2. Patient expectations, mindset, and empathy with the treatment plan will greatly influence treatment outcomes. H3. Physician expectations of patients play a positive role in the emergence of the principle of multifaceted consistency. We then consider the moderating effect of different sources of expectations on physicians' career choices. The gap between healthcare platforms and physician expectations. Prior literature on social structure suggests that socially advantaged individuals should have a competitive advantage that can be used to obtain more resources [ 32 ].Specifically, members of a better organization tend to have better control over resources. For example, Jiang et al.[ 22 ] noted how cities control more healthcare knowledge and resources as this study evolved. As a result, a good hospital may have better doctors, more advanced equipment, and a better healthcare environment. Patients also expect to choose doctors belonging to higher level hospitals or platforms in their decision making process [ 43 ].Patients may be more inclined to choose doctors if they expect more from online platforms, believing that the services they provide are more specialized or more trustworthy. As a result, high level hospitals and platforms will help physicians gain more advantage over the competition. Specifically, when patients choose to consult physicians affiliated with higher-level hospitals, the conflict between patients' expectations and actual services may appear to decrease due to the higher actual quality of care provided by these physicians. Similarly, physicians' own expectations of this kind are reduced when they are attached to a higher-ranking platform. Conversely, when physicians have positive expectations about their career prospects, they believe that they can increase their visibility, professional prestige, and even expand their career opportunities by participating in online platforms. This will lead to a change in the physician's career outlook. Therefore, we expect that when patients perceive and respond positively to a physician's positive expectations, this may motivate physicians to become more engaged and achieve greater success in their careers [ 44 ].This leads to the following hypothesis: H4. Expectations from platforms, patients, and physicians themselves have an impact on physicians' career prospects. This study further considered whether the Marion effect moderates the impact of online physician ratings on treatment outcomes. The Chinese healthcare market has a strict hierarchy. In China, doctors have two titles: clinical title2 and academic title.3 The level of clinical title reflects a doctor's medical experience, while the level of academic title reflects a doctor's academic level. In general, patients are more willing to trust doctors with senior titles. In other words, patients will prioritize doctors with higher titles for online or offline consultations. In addition, for a doctor with a higher clinical and academic title, he/she has a higher level of medical proficiency and experience in treatment and counseling. Online consultations provided by these physicians are more likely to meet patients' expectations. Thus, the influence of ratings on patients' choice of physician and their subsequent treatment outcomes may be due to the Pygmalion effect. The effect of patient choice on physician ratings may be greater for physicians with higher clinical and academic titles. Therefore, we expected that the level of a physician's clinical title and academic title would positively moderate the positive correlation between physician ratings and patient preference choices. This leads to the following hypotheses: H5. The Pygmalion effect will lead to higher and higher ratings of good physicians, which in turn reverses the effect.H6. Physician expectations, patient self-efficacy, and adherence during subsequent physician-to-patient follow-up will have a two-sided effect on recovery outcomes. 4. Data description and summary statistics We conducted independent text mining to obtain data from China's largest healthcare portal, "Good Doctor," as well as two large online healthcare platforms in Europe and the United States, webmd and practo, to study the impact of the Pygmalion effect in healthcare." Good Doctor" is an independent healthcare platform connecting patients and doctors, with more than 240,000 registered doctors covering about 10,000 regular hospitals. Doctors' basic information (e.g., affiliated hospitals, areas of specialization, clinical titles, academic titles, etc.) can be displayed on the platform's personal page. Through the platform, patients can find suitable doctors for consultation and diagnosis. Consultations can be conducted online through the interface provided by the platform or over the phone, for which a fee is usually required. After the consultation, the patient or his/her relatives can vote for the doctor through the platform's service interface, or even write comments such as thank you letters to the doctor, or buy virtual gifts for the doctor. We developed a python-based crawler program to collect data from these three websites. To ensure a fair sample, we collected data from all doctors. We then randomly selected 5000 doctors over an 8-month period (from January 2023 to August 2020) from their personal pages and information about their affiliated hospitals. Our sample was organized by month. The collection process lasted about three days. Therefore, data updates during the capture period are unlikely to interfere with our findings. In addition, for webmd and practo, we used two main approaches: text mining based on big data technology and sentiment analysis based on natural language processing. First, we mined text from webmd pages as well as doctor-patient communication interface pages, and then filtered out information containing emotions obtained through crawling. We left the information related to the patient's emotional expression and then categorized this information according to its source, thus avoiding redundant emotional samples and reducing data errors. We excluded from the sample physicians who did not receive any patient comments or feedback during the study period, as the data from these physicians were not relevant to our current research considerations. The final sample size consisted of 10,243 physicians and 50,625 observations. The sample included physician attributes, healthcare costs, physician-patient communication, ways of consulting physicians, healthcare choices, and physician decision-making. Physician attributes included online ratings, service satisfaction, outcome satisfaction, attitude satisfaction, registration time, total visits, academic title, clinical title, working hospital or online platform, number of patients, number of articles, and number of thank you notes. There are two main sources of information for doctors on the "Good Doctor" platform: one is through referrals from close friends, relatives, etc., and the other is through self-knowledge of choosing a doctor. Table 1 shows the descriptive statistics of the key variables. We performed large-scale text data mining of the information on the searchable communication pages of these three websites to extract useful information from them. This process involves identifying, extracting, and reasoning about meaningful conversations, sentiments, etc. from the text. Our task focuses on machine learning-based text classification and sentiment analysis, and later on linear regression analysis algorithms based on the above variables. Tables 1 and 2 show the relevant mining statistics for the key variables and the corresponding sentiment analysis results. Table 1 Descriptive Statistics Variable Minimum Maximum Mean Standard deviation Total number of patients 0 125766 35861.5 45876.2 Online rating 1.8 5 3.57 0.247 Online service satisfaction 0 1 0.465 0.517 Performance satisfaction 0 1 0.671 0.457 Attitude satisfaction 0 1 0.654 0.429 Registration time 567 5781 3587 2653 Total visits 1580 3.47E + 09 2484067 5003597 Cost of treatment 64 3095 257 108 Total number of articles 0 6849 56.7 138.8 Number of thank-you letters 0 9083 219.7 468.3 5. Research Methodology 5.1.Linear regression analysis and Modeling of 6 Factors Affecting Patient Evaluation Scores In order to address our first question, the model was constructed by first analyzing (1) treatment cost (2) treatment process expectations (3) total number of visits to the corresponding physician (4) total number of visits to the corresponding physician (5) treatment access (matching expectations as 1) (6) adherence as the independent variable, and treatment efficacy satisfaction, attitudinal satisfaction, and evaluation scores as the dependent variables: Treatment efficacy satisfaction = a0 + a1* (treatment cost) + a₂* (treatment process expectations) + a3* (total number of visits to the corresponding doctor) + a4* (doctor's online rating) + a5* (access to treatment modalities) + a6* (adherence) (1) Attitudinal satisfaction = a7 + a8* (treatment cost) + a9* (treatment process expectations) + a10* (total number of visits to the corresponding doctor) + a11* (doctor's online rating) + a12* (treatment modality access) + a13* (adherence) (2) Evaluation scores = a14 + a15* (treatment cost) + a16* (treatment process expectations) + a17* (total number of visits to the corresponding physician) + a18* (physician online rating) + a19* (treatment modality access) + a20* (adherence) (3) Treatment Effectiveness Satisfaction Efficacy, Process Expectation, Physician Online Score and Treatment Modality Access have a significant effect on treatment effectiveness satisfaction, and Treatment Cost, Treatment Process Expectation, Corresponding Total Number of Physician Visits, Treatment Modality Access and Adherence have a significant effect on the evaluation scores. Thus, on average, for every percentage point increase in treatment process expectations, the total number of treatment efficacy satisfaction increases by 2.25 percentage points, while the evaluation score increases by 5.20; for every percentage point increase in physician online ratings, the total number of treatment efficacy satisfaction increases by 12.39 percentage points, while the evaluation score increases by 0.97; for every percentage point increase in treatment modality access, the total number of treatment efficacy satisfaction total would increase by 2.10 percentage points, while the evaluation score would increase by 10.13; for each percentage point increase in treatment cost, the evaluation score would increase by 2.60; and for each percentage point increase in adherence, the evaluation score would increase by 4.20. Meanwhile, the impact factor model with evaluation score as the dependent variable. Satisfaction with treatment efficacy = -0.58 - (treatment cost) + 2.25* (treatment process expectations) + 0.64* (total number of visits to the corresponding doctor) + 12.39* (doctor's online rating) + 2.10* (access to treatment modalities) − 0.65* (adherence) (1) The best fit of R2_a = 0.983 indicates that the model is statistically significant. Close to this is the model with treatment efficacy satisfaction as the dependent variable. (Evaluation Score) = 3.70 + 2.60* (Cost of Treatment) + 5.20* (Expectation of Treatment Procedure) + 1.89* (Corresponding to Total Number of Doctor Visits) + 0.97* (Doctor's Online Score) + 10.13* (Access to Treatment Modality) + 4.20* (Adherence) (3) R2_a = 0.896, which is also a better fit, and the model with Attitude Satisfaction as a Dependent Variable. (Attitudinal satisfaction) = 1.82 + 0.66* (treatment cost) − 0.09* (treatment process expectations) + 0.72* (total number of visits to the corresponding doctor) + 1.54* (doctor's online rating) − 1.22* (access to treatment modalities) − 0.86* (adherence) (2) R2_a = 0.0682 , which is not significant. Table 3 Effect of six independent variables, including treatment process expectations, on patients' treatment efficacy satisfaction (1), attitude satisfaction (2), and evaluation scores (3). The numbers in parentheses are the corresponding standardized coefficients. VARIABLES (1) (2) (3) 1n(treatment cost) -0.353 5.696 0.292** (-1.00) (0.66) (2.60) Treatment Process 0.616** -0.576 0.453*** Expectations (2.25) (-0.09) (5.20) 1n (Total number of corresponding physician visits) 0.210 5.796 0.198* (0.64) (0.72) (1.89) Doctor's Online Scores 0.837*** 2.528 0.021 (12.39) (1.54) (0.97) Access to treatments 0.678** -9.588 1.043*** (2.10) (-1.22) (10.13) Compliance -0.134 -4.296 0.275*** (-0.65) (-0.86) (4.20) Constant -0.352 26.963* 0.716*** (-0.58) (1.82) (3.70) Observations 99 99 99 R-squared 0.896 0.125 0.984 F test 0 0.0502 0 r2_a 0.890 0.0682 0.983 F 132.5 2.196 932.4 Note:***p < 0.01,**p < 0.05,*p < 0.1 5.2 Hypothesis validation and model fit assessment In order to explore the effects of the main independent variables on the dependent variable in different hypotheses, as well as to examine the moderating effects of the moderating variables, the moderating effect analysis was carried out by linear regression, and a linear regression model was constructed with the following model expression: (target variable) = a21 + a22 * (research main independent variable) + a23 * (moderating factor) + a24 (interaction factor) + a25 (control variable 1) ... + µ + ε (4) where the target variable and the research main independent variable need to be based on the needs of different hypotheses; moderating factor is the non-negligible significance in the hypothesis affecting the target variable. The interaction factor is the part of the interaction between the main independent variable and the moderating variable that affects the target variable, a21 is the intercept term, a22, a23, a24, a24... are the model coefficients, µ denotes the capture of unobserved individual-specific effects, and ε it denotes the residual random error term. In order to test hypothesis one, after centering the data of different variables and detecting multiple covariance, we used the patient treatment process expectation on the telemedicine platform as the independent variable and the total number of visits to the corresponding doctors as the target variable. Moderating effect analysis was performed using linear regression, with the moderating factor being access to treatment modalities. At the same time, considering other possible influences, we controlled for the following variables: (1) adherence (2) physician online scores [note the presence or absence of a high correlation between these variables (Pearson = 0.99, p < 0.001), and the need to use both variables as covariates if they are present]. Table 4 Analysis of the moderating effect of Zscore (treatment modality access) on Zscore (treatment process expectations) on Zscore (corresponding to total physician visits). variant coeff se t P (constant) -0.001 0.087 0.011 0.991 Zscore (Doctor's Online Scores) 0.262 0.091 2.878 0.005 Zscore(Treatment costs) -0.001 0.089 -0.007 0.994 Zscore(Treatment Process Expectations) -0.397 0.092 -4.322 0.000 Zscore(Access to treatments) -0.051 0.089 -0.570 0.570 Expectations and approaches 0.014 0.091 0.153 0.878 R 0.536d R2 0.288 Durbin Watson. 1.595 According to the results of Table IV, it can be seen that treatment process expectation has a significant positive predictive effect on the total number of physician visits (P < 0.001),while the moderating variable treatment modality access has no significant positive predictive effect, and the interaction variable of the two is not significant at the level of P < 0.01, which indicates that there is no moderating effect of treatment modality access on the relationship between the treatment process expectation and the total number of physician visits. Substituting (4) indicates a linear regression model as: (corresponding to the total number of physician visits) = 0.087 + 0.092 * (treatment process expectations) + 0.089* (treatment modality access) + 0.091 (interaction factors) + 0.091 (physician online ratings) + 0.089 (cost of treatment $) + µ + ε (5) where µ denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive, R2 = 0.288, and the model fit is fair and does not demonstrate the presence of moderating variables. It is noteworthy that the actual line map is affected by the coefficients provided in the model, which are calculated based on the actual data. Here, we will simplify the model and consider only the basic linear relationship between the variables, not including the random error terms༂µ༂ and༂ε༂, to facilitate visualization. The figure below shows the effect of the treatment process expectation on the total number of corresponding physician visits, and examines the role of treatment modality access as a regulatory variable in this relationship. The graph illustrates the effect of treatment expectation on the number of doctor visits, considering both scenarios with and without the interaction term involving the method of obtaining treatment as a moderating variable. The dashed line represents the relationship without considering the interaction between treatment expectation and the method of obtaining treatment, while the solid red line includes this interaction. As shown, the inclusion of the interaction term modifies the slope of the relationship, suggesting that the effect of treatment expectation on doctor visits is indeed influenced by how patients access treatment methods. In summary, the higher the expectations of the treatment process, the higher the total number of physician visits, and the access to treatment modalities has a smaller effect on the total number of physician visits, and Hypothesis I is supported. In order to test hypothesis two, "patients' expectations, mindset, and empathy with the treatment program will greatly affect the treatment effect," we conducted a moderating effect analysis using treatment process expectations as the independent variable and satisfaction with governance effectiveness as the target variable, while examining the moderating effect of adherence and controlling for the following variables: (1) total number of visits to the corresponding doctor (2) attitude satisfaction (3) treatment cost (4) doctor's online rating. A linear regression model was constructed to explore the effect of treatment process expectations on satisfaction with governance effectiveness. The results of the analysis are presented below: Table 5 Analysis of the moderating effect of Zscore (adherence) on Zscore (treatment process expectations) on Zscore (satisfaction with governance effectiveness) variant coeff se t P (Constant) -0.001 0.019 -0.081 0.936 Zscore(Doctor's online score) 0.298 0.043 7 0 Zscore(treatment cost $ ) -0.015 0.019 -0.788 0.433 Zscore(total number of doctor visits) -0.013 0.023 -0.557 0.579 Zscore(attitude satisfaction) 0.004 0.027 0.147 0.884 Zscore(Expectation of treatment process) -0.019 0.026 -0.736 0.464 Zscore(Adherence) 0.706 0.043 16.58 0 Expectations and compliance -0.02 0.019 -1.052 0.296 R 0.984d R2 0.968 Durbin Watson. 1.403 Based on the results of the analysis, it can be seen that the positive predictive effect of treatment process expectations on satisfaction with governance efficacy is not significant (P ≥ 0.01),while the moderating variable of adherence has a significant positive predictive effect (P < 0.001), and the interaction variable of the two is not significant at the level of P < 0.01, which indicates that there is no moderating effect of adherence between treatment process expectations and satisfaction with governance efficacy. Substituting (3) indicated a linear regression model as: (Satisfaction with governance efficacy) = 0.019 + 0.026 * (Treatment process expectations) + 0.043* (Adherence) + 0.019 (Interaction factors) + 0.043 (Physician online ratings) + 0.019 (Cost of treatment $) + 0.023 (Corresponding to the total number of visits to the physician) + 0.027 (Attitudinal satisfaction) + µ + ε (6) where µ denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive, R2 = 0.968, and ΔR2 obeys the Durbin-Watson distribution, which is a good fit and proves the existence of the moderator variable adherence. Figure 3 Since compliance is a significant positive predictor of governance efficacy satisfaction, we will focus on demonstrating the impact of treatment process expectation and compliance on governance efficacy satisfaction and trying to show no significant interaction between the two, as shown in Fig. 3 . The graph illustrates the impact of treatment expectation and compliance on governance efficacy satisfaction. The solid line represents the relationship between treatment expectation and governance efficacy satisfaction, demonstrating how increases in treatment expectation contribute to higher levels of governance efficacy satisfaction. The dashed line highlights the effect of compliance on governance efficacy satisfaction separately, indicating that higher compliance levels are associated with increased governance efficacy satisfaction. Adherence reflects the degree of patients' empathy with the treatment program, and adherence has a positive impact on treatment outcomes, indicating that the higher the empathy between patients and the treatment program, the higher the adherence and the higher the satisfaction with governance efficacy. The results can partially support hypothesis two. A moderating effects analysis with treatment process expectations as the independent variable and governance efficacy satisfaction as the target variable was conducted to test Hypothesis III, while examining the moderating effects of physicians' online ratings and treatment costs and controlling for the following variables: (1) total number of corresponding physician visits (2) attitudinal satisfaction (3) adherence (4) physician online ratings (5) access to treatment modalities Table 6 Analysis of the moderating effect of Zscore (doctor online score) and Zscore (treatment cost $ ) on Zscore (treatment process expectations) on Zscore (satisfaction with governance effectiveness) variant coeff se t P (Constant) -0.001 0.019 -0.053 0.958 Zscore(total number of doctor visits) -0.011 0.023 -0.483 0.63 Zscore(attitude satisfaction) 0.005 0.027 0.17 0.865 Zscore(Adherence) 0.705 0.043 16.34 0 Zscore(access to treatment) 0.027 0.019 1.41 0.162 Zscore(Expectation of treatment process) -0.02 0.026 -0.746 0.458 Zscore(Cost of treatment) -0.015 0.019 -0.799 0.427 Zscore(Doctor's online rating) 0.301 0.043 6.955 0 Expectation and Score -0.02 0.021 -0.962 0.339 Expectation and Cost 0.006 0.022 0.266 0.791 R 0.984d R2 0.969 Durbin Watson 1.421 Based on the results of the analysis, it can be seen that the positive predictive effects of treatment process expectations and treatment cost on satisfaction with governance effectiveness were not significant (P ≥ 0.01),while the moderating variable, physician online ratings, had a significant positive predictive effect (P < 0.001), and the interaction variable between the two was not significant at the P < 0.01 level, which suggests that there is no moderating effect of either physician online ratings or treatment costs in the relationship between treatment process expectations and satisfaction with governance effectiveness. There was no moderating effect between either satisfaction. Substituting (4) indicated a linear regression model as: (Satisfaction with governance efficacy) = 0.019 + 0.026 * (Expectation of treatment process) + 0.043 (Physician online rating) + 0.019 (Treatment cost$) + 0.021 (Interaction: expectation and rating) + 0.022 (Interaction: expectation and cost) + 0.023 (Corresponding to the total number of visits to the physician) + 0.027 (Attitudinal satisfaction) + 0.043* ( adherence) + 0.019 (treatment modality access) + µ + ε (7) where µ denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive with R2 = 0.969, which is a good fit and a robust model. It indicates that physician online ratings have a positive impact on treatment outcomes, and the higher the physician online rating, the higher the patient's satisfaction with governance effectiveness. The results can partially support hypothesis three. Figure 4 This linear relationship will be simulated by constructing a hypothetical dataset and showing how the primary variables-treatment process expectations, physician online ratings, and treatment costs-alone and in combination affect governance efficacy satisfaction. In particular, since the effect of the interaction variables was shown to be not significant by the analysis, we focused on the direct effects of the primary variables rather than their interaction effects. Figure 4 The graph illustrates the impacts of treatment expectation, doctor online rating, and treatment cost on treatment efficacy satisfaction. Each line represents a different relationship: The solid line shows how treatment efficacy satisfaction changes with varying levels of treatment expectation, indicating a direct relationship based on the model. The dashed line depicts the relationship between treatment efficacy satisfaction and doctor online rating, highlighting the significant positive effect that higher online ratings have on treatment satisfaction. The dot-dashed red line illustrates the effect of treatment cost on treatment efficacy satisfaction, suggesting a more complex relationship that might not be as straightforward as the other two factors. This visualization underscores the importance of both the patient's treatment expectation and the perceived quality of the doctor (as reflected in online ratings) in influencing treatment outcomes. It also acknowledges the role of treatment cost, although its direct impact on satisfaction might be nuanced and requires further investigation. Notably, the analysis indicated that the interaction effects between these variables and treatment expectation were not significant, focusing instead on their direct influences. In order to test hypothesis four, we use the patient treatment process expectation on the telemedicine platform as the independent variable, and the doctor online score and evaluation score as the target variables, respectively, to study the moderating effects of the corresponding total number of doctor visits and the treatment cost between the treatment process expectation and the doctor online score, and between the treatment process expectation and the evaluation score. At the same time, considering other possible influences, we controlled for the following variables: (1) adherence (2) treatment modality access (3) attitude satisfaction (4) treatment efficacy satisfaction. The results of the analysis are shown in Tables VII and VIII: Table 7 Analysis of the moderating effect of Zscore (corresponding to the total number of doctor visits) and Zscore (treatment cost $ ) on Zscore (treatment process expectations) on Zscore (doctor online score) variant coeff se t P (Constant) -0.001 0.037 -0.019 0.985 Zscore(Adherence) -0.255 0.171 -1.493 0.139 Zscore(Treatment access) -0.02 0.039 -0.528 0.599 Zscore(Attitude satisfaction) 0.021 0.054 0.393 0.695 Zscore(Satisfaction with treatment efficacy) 1.174 0.171 6.855 0 Zscore(Expectation of treatment process) 0.017 0.053 0.318 0.751 Zscore(Total number of doctor visits) 0.015 0.047 0.321 0.749 Zscore(Treatment cost $ ) 0.015 0.038 0.379 0.705 Expectation and visit 0.004 0.033 0.119 0.905 Expectation and Cost -0.035 0.045 -0.777 0.439 R 0.936d R2 0.875 Durbin Watson 1.628 Based on the results of the analysis in Table VII, it can be seen that none of the positive predictive effects of treatment process expectations, total number of visits to the corresponding physician, and cost of treatment on satisfaction with governance effectiveness were significant (P ≥ 0.01), and the interaction variable between the two was not significant at the P < 0.01 level, suggesting that there was no moderating effect of either total number of visits to the corresponding physician or cost of treatment between treatment process expectations and the physician's online rating. A linear regression model was expressed as: (Doctor Online Score) = 0.037 + 0.053 * (Treatment Process Expectation) + 0.047 (Corresponding to Total Number of Doctor Visits) + 0.038 (Treatment Cost$) + 0.033 (Interaction Factor: Expectation and Visits) + 0.045 (Interaction Factor: Expectation and Cost) + 0.171* (Adherence) + 0.039 (Access to Treatment Modality) + 0.054 ( Attitude satisfaction) + 0.171 (Treatment efficacy satisfaction) + µ + ε (8) where µ denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive with R2 = 0.875, which is a good fit. Table 8 Analysis of Moderating Effects of Zscore (Corresponding to Total Physician Visits) and Zscore (Treatment Cost $ ) on Zscore (Treatment Procedure Expectation) on Zscore (Evaluation Score) variant coeff se t P (Constant) 0.011 0.051 0.213 0.832 Zscore(Adherence) 0.133 0.238 0.560 0.577 Zscore(Treatment access) -0.122 0.052 -2.352 0.021 Zscore(Attitude satisfaction) -0.043 0.073 -0.597 0.552 Zscore(Satisfaction with treatment efficacy) 0.739 0.235 3.143 0.002 Zscore(Expectation of treatment process) -0.009 0.072 -0.123 0.902 Zscore(Total number of doctor visits) -0.004 0.063 -0.067 0.947 Zscore(Treatment cost) 0.079 0.052 1.523 0.131 Expectation and visit 0.006 0.044 0.137 0.891 Expectation and Cost 0.069 0.060 1.151 0.253 R .882d R2 0.778 Durbin Watson 2.074 Based on the results of the analysis, it was found that the positive predictive effects of treatment process expectations, total number of visits to the corresponding physician, and treatment cost on the evaluation scores were not significant (P ≥ 0.01), while the interaction variables of the two were not significant at the P < 0.01 level, indicating that there were no moderating effects of total number of visits to the corresponding physician and treatment cost between treatment process expectations and evaluation scores. The linear regression model was: (Evaluation Score) = 0.051 + 0.072 * (Treatment Procedure Expectations) + 0.063 (Corresponding to Total Number of Physician Visits) + 0.052 (Treatment Cost$) + 0.044 (Interaction Factors: Expectations and Visits) + 0.060 (Interaction Factors: Expectations and Costs) + 0.238* (Adherence) + 0.052 (Access to Treatment Modalities) + 0.073 (Attitude satisfaction) + 0.235 (treatment efficacy satisfaction) + µ + ε (9) where µ denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive with R2 = 0.778, which is a fair fit. Therefore the results do not support hypothesis four. Figure 5 This linear relationship was simulated by constructing a hypothetical dataset and demonstrating how the primary variables — treatment process expectations, corresponding physician total visits, treatment costs — alone, and in combination, influenced the physician online score. In particular, emphasis was placed on the direct effect of the treatment process expectations and its potential interaction effects with the total number of visits to the corresponding physician and treatment costs. The graph depicts the impact of treatment expectation on the doctor's online rating, illustrating a linear relationship based on the provided model. This visualization suggests that as treatment expectation increases, there's a corresponding increase in the doctor's online rating, reflecting the positive coefficient associated with treatment expectation in the model. This model controls for other factors such as the number of doctor visits and treatment cost, indicating that the primary focus is on the direct relationship between treatment expectation and online rating. The visualization simplifies the model by not explicitly showing the effects of control variables and potential interaction terms, focusing instead on the significant direct impact of treatment expectation. The visualization simplifies the model by not explicitly showing the effects of control variables and potential interaction terms, focusing instead on the significant direct impact of treatment expectation. In order to test hypothesis five, "The Pygmalion effect will lead to higher and higher ratings of good doctors, and there is a reverse exacerbation effect," we constructed a model to study treatment efficacy satisfaction and doctors' online ratings as the independent and target variables, respectively, using linear regression while controlling for the following variables: (1) access to treatment modalities (2) Treatment cost (3) Attitude satisfaction (4) Treatment process expectations. The results are shown in Table IX and Table X: Table 9 Linear regression analysis of Zscore (treatment efficacy satisfaction) on Zscore (physician online score) variant coeff se t P (Constant) -5.71E-16 0.037 0 1 Zscore(access to healing modalities) 0.012 0.037 0.318 0.751 Zscore(treatment cost ) -0.001 0.038 -0.021 0.983 Zscore(Attitude satisfaction) -0.014 0.054 -0.267 0.79 Zscore(Expectation of treatment process) -0.022 0.052 -0.421 0.675 Zscore(total number of doctor visits) 0.04 0.046 0.872 0.386 Zscore(Satisfaction with treatment efficacy) 0.917 0.040 22.698 0 R .934b R2 0.872 Durbin Watson. 1.633 Table 10 Linear regression analysis of Zscore (physician online score) on Zscore (satisfaction with treatment efficacy) variant coeff se t P (Variant) 5.58E-16 0.037 0 1 Zscore(access to healing modality) -0.014 0.038 -0.381 0.704 Zscore(treatment cost ) 0.005 0.038 0.135 0.893 Zscore(attitude satisfaction) 0.025 0.054 0.458 0.648 Zscore(Expectation of treatment process) 0.01 0.053 0.188 0.851 Zscore(total number of doctor visits) 0.008 0.046 0.168 0.867 Zscore(Doctor's online rating) 0.925 0.041 22.698 0 R .933b R2 0.871 Durbin Watson. 1.623 Based on the results of the analysis, it was found that treatment efficacy satisfaction had a significant positive predictive effect (p < 0.001) on physician online ratings and physician online ratings on governance efficacy satisfaction, with linear regression models as: (doctor online score) = 0.037 + 0.040 (treatment efficacy satisfaction) + 0.037 (treatment modality access) + 0.038 (treatment cost$) + 0.054 (attitude satisfaction) + 0.052 * (treatment process expectation) + 0.046 (corresponding to the total number of doctor visits) + µ + ε (10) (Satisfaction with treatment efficacy) = 0.037 + 0.041 (doctor's online rating) + 0.038 (access to treatment modalities) + 0.038 (cost of treatment $) + 0.054 (attitudinal satisfaction) + 0.053 * (expectations of the treatment process) + 0.046 (corresponding to the total number of doctor's visits) + µ + ε (11) where µ denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive with R2 = 0.871, which is a good fit, indicating the presence of moderating variables and high reliability of the model. Figure 6 In both models, we considered the controlling role of other variables such as treatment modality access route, treatment cost, attitude satisfaction, treatment process expectation and the total number of corresponding physician visits. The results of the model showed that there is a significant positive relationship between treatment efficacy satisfaction and doctors 'online score, which supports the existence of Pygmalion effect, that is, doctors' praise will lead to The two graphs illustrate the mutual influence between doctor online rating and treatment efficacy satisfaction, supporting the hypothesis that a positive loop exists—higher treatment efficacy satisfaction leads to higher doctor online ratings and vice versa. The first graph (left) shows the effect of treatment efficacy satisfaction on doctor online rating. As treatment efficacy satisfaction increases, the doctor's online rating also increases, highlighting the direct positive relationship between these variables. The second graph (right) demonstrates the reverse relationship, where an increase in doctor online rating contributes to higher treatment efficacy satisfaction. This again confirms the positive feedback loop between these variables, aligning with the Pygmalion effect hypothesis. These visualizations encapsulate the essence of Hypothesis Five, demonstrating the interconnectedness and reinforcing nature of treatment satisfaction and physician ratings within the context of remote medical platforms. Therefore, hypothesis five is supported. In order to test hypothesis six, we investigated the moderating effects of adherence and attitudinal satisfaction between treatment process expectations and treatment efficacy satisfaction on the telemedicine platform with patient treatment process expectations as the independent variable and treatment efficacy satisfaction as the target variable, respectively. Meanwhile, considering other possible influencing factors, we controlled for the following variables: (1) treatment modality access (2) doctor online rating (3) total number of visits to the corresponding doctor (4) treatment cost. The results of the analysis are shown in Table 11 : Table 11 Analysis of moderating effects of Zscore (adherence) and Zscore (attitude satisfaction) on Zscore (treatment process expectations) on Zscore (treatment efficacy satisfaction). variant coeff se t P (constant) -0.002 0.019 -0.105 0.917 Zscore(Access to treatment) 0.029 0.019 1.511 0.134 Zscore(Doctor's online score) 0.297 0.042 6.991 0 Zscore(Total visits to corresponding doctors) -0.011 0.023 -0.485 0.629 Zscore(Treatment cost) -0.014 0.019 -0.741 0.461 Zscore(Expectation of treatment process) -0.019 0.026 -0.744 0.459 Zscore(Compliance) 0.706 0.043 16.512 0 Zscore(Attitude satisfaction) 0.006 0.028 0.215 0.83 Expectation and Attitude -0.013 0.025 -0.504 0.615 Expectation and adherence -0.016 0.024 -0.683 0.496 R .985d R2 0.969 Durbin-Watson 1.429 Based on the results of the analysis, it can be seen that the positive predictive effect of treatment process expectations and attitude satisfaction on satisfaction with governance efficacy is not significant (P ≥ 0.01), while the moderator variable of adherence has a significant positive predictive effect (P < 0.001), and the interaction variable of the two is not significant at the level of P < 0.01, which indicates that there is no moderating effect of adherence and attitude satisfaction between treatment process expectations and governance efficacy satisfaction Neither had a moderating effect. Substituting (4) indicates a linear regression model as: (Satisfaction with treatment efficacy) = 0.019 + 0.026* (treatment process expectations) + 0.043 (adherence) + 0.028 (attitudinal satisfaction) + 0.025 (interaction: expectations and attitudes) + 0.024 (interaction: expectations and adherence) + 0.019 (access to treatment modalities) + 0.023 (corresponding to the total number of physician visits) + 0.019 ( cost of treatment $) + 0.042 (physician online rating) + µ + ε (12) where µ denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term was positive with R2 = 0.969, which is a good fit and a reliable model. Figure 7 The graph showcases the relationship between treatment expectation and treatment efficacy satisfaction, with adjustments for compliance and attitude satisfaction. This visualization indicates that as treatment expectation increases, there is a corresponding increase in treatment efficacy satisfaction, factoring in the positive influences of compliance and attitude satisfaction. This model highlights the significant role of patient compliance and their satisfaction with the treatment's approach, supporting the hypothesis that higher compliance and a positive attitude towards the treatment process contribute positively to the overall treatment efficacy satisfaction. It demonstrates the interconnectedness of these factors in the remote healthcare context, where patient expectations, compliance, and attitudes are crucial determinants of treatment outcomes. It indicates that patient adherence has a positive effect on treatment outcomes, and the greater the patient adherence, the higher the satisfaction with treatment efficacy. The results can partially support part of hypothesis six, which can be described as "the follow-up process of the doctor's expectations of the patient, the patient's self-efficacy, adherence and the patient's recovery effect have a positive relationship". 6. Discussion 6.1. key findings Our study explored multiple factors influencing patients' satisfaction with physicians' treatment efficacy on a telemedicine platform by constructing a model based on multiple linear regression and moderated regression. By analyzing the data, we found that the variables of access to treatment, doctor online rating, total visits to the corresponding doctor, treatment cost, expectations of the treatment process, adherence, and attitudinal satisfaction had a significant effect on the satisfaction with treatment efficacy. Among these variables, physician online rating and adherence were significant positive predictors of satisfaction with treatment efficacy, whereas the relationship between treatment expectations and satisfaction with treatment efficacy was moderated by adherence. In addition, we found no moderating effect of patient adherence between treatment process expectations and treatment efficacy satisfaction. Through robustness checks, we further confirmed the reliability and stability of our findings. These findings contribute to a deeper understanding of the formation mechanism of patients' satisfaction with doctors' treatment efficacy on telemedicine platforms, and provide useful insights and suggestions for improving the quality of telemedicine services. From the results of models (1) and (2), it can be seen that treatment process expectations have a significant positive effect on both treatment satisfaction and evaluation scores. This suggests that the higher the patients' expectations of the treatment process, the higher their treatment satisfaction and evaluation scores will be accordingly. This result is consistent with our hypothesis, suggesting that patients' expectations of the treatment process have a significant effect on their efficacy satisfaction and evaluation, and that patients are more inclined to choose doctors and treatment programs on telemedicine platforms that meet their original expectations, and that this reduces the efficacy of their treatment when they suffer from serious illnesses. Satisfaction with and evaluation of treatment can indirectly reflect the effectiveness of treatment, thus proving Hypothesis 2: Patient's expectations will greatly influence the effectiveness of treatment. However, we also need to be aware of the influence of other factors. For example, doctors' online ratings demonstrated a significant positive association between treatment effectiveness satisfaction and evaluation scores. This implies that physician professionalism and the quality of online services are also important factors in patient satisfaction and evaluation. Similarly, access to treatment modalities had a significant positive effect on evaluation scores, suggesting that patients' ease of access to treatment modalities affects their evaluations of treatment, thus validating our hypothesis that "patients are more likely to receive medical information that is consistent with their existing beliefs or expectations, and that this information affects the strength of the Pygmalion effect". The coefficients of treatment cost and physician online rating in the two models are positive, in which treatment cost has a significant effect on evaluation scores, and physician online rating has a significant effect on satisfaction with treatment efficacy, indicating that treatment cost has a positive effect on evaluation scores, and physician online rating has a positive effect on satisfaction with treatment efficacy to varying degrees. The treatment cost can reflect the doctor's treatment expectation, and the increase of cost can affect the doctor's treatment cost and motivation, and the doctor's online score also has a positive incentive effect on the doctor's treatment expectation, so it can indirectly reflect the reasonableness of hypothesis three, "The doctor's expectation of the patient plays a positive role in the creation of the principle of consistency of the multiple aspects". Meanwhile, the positive and significant effect of physicians' online ratings on satisfaction with treatment efficacy also reflects that the higher the physicians' online ratings, the higher the patients' satisfaction with treatment efficacy, thus proving that physician ratings have an exacerbating effect on patients' satisfaction and evaluation in hypothesis five. Cost and online scores led to an increase in physician expectations of patients, and the positive effect of physician expectations on recovery outcomes in hypothesis six was also verified. Adherence has a significant positive effect on evaluation scores, indicating that patients' own behavioral adherence will improve their evaluation of treatment, but the effect of adherence on satisfaction with treatment efficacy and attitudinal satisfaction is not statistically significant, while the recovery effect is influenced by a large number of factors, and Hypothesis six, "Subsequent adherence in the process of physician's follow-up with the patient will have a two-fold effect on the recovery effect "To be further investigated. Hypothesis four, "Expectations from platforms, patients, and physicians themselves have an impact on physicians' career prospects," needs to be verified while controlling for other variables to determine the effects of expectations of the treatment process, physicians' online ratings, and attitudinal satisfaction on the target variables corresponding to the total number of physician visits and physicians' online ratings; the first half of hypothesis five, "Pygmalion", is a hypothesis. "The Pygmalion effect will result in higher and higher ratings for good doctors" needs to be verified for the significance of treatment efficacy satisfaction on the target variable doctor online scores; and the second half of hypothesis six after the doctor's expectations of the patient, patient self-efficacy, and adherence will have positive and negative effects on the recovery effect also needs to be continued to analyze the effects of patient adherence, attitudinal satisfaction, and doctor's expectations of the patient on the treatment effectiveness of the reverse effect of satisfaction, if necessary, can increase the variables to increase the representativeness, in order to fully reflect the situation of the patient's recovery effect, while the patient's self-efficacy has not yet been confirmed to have a significant impact on the recovery effect. 6.2 Limitations and future directions To further explore the effect of treatment process expectations on evaluation scores, we conducted moderated effects analyses for each of the six hypotheses. The results of these analyses indicated that: treatment process expectations had a positive predictive effect on the total number of visits to the corresponding physician, while treatment modality access had a lesser effect on the total number of visits to the physician, and Hypothesis I was supported; the stronger the patient's empathy with the treatment plan, the higher the adherence, and the higher the satisfaction with governance efficacy, and thus the results could partially support Hypothesis II; the physician's online score had a positive impact on the treatment efficacy, and the results could partially support Hypothesis three; the results do not support hypothesis four; treatment effectiveness satisfaction has a significant positive predictive effect on physician online ratings and physician online ratings have a positive predictive effect on governance effectiveness satisfaction, so hypothesis five is supported; patient adherence has a positive effect on treatment effectiveness, and the results can partially support hypothesis six. This can be described as "physician expectations, patient self-efficacy, and adherence are positively correlated with recovery outcomes during subsequent physician follow-up with the patient." Expectation of the treatment process was an independent influence on the evaluation scores, and its effect was statistically significant. To ensure the stability and reliability of our findings, we also conducted an R2 fit comparison, and we found that all hypotheses except Hypothesis 1 had a high fit, indicating the robustness of Models 6–11. This further supports our hypotheses and conclusions about the effect of treatment process expectations on evaluation scores. However, the study of the Pygmalion effect on physicians' career prospects we have left at the theoretical level, i.e., through sentiment analysis, it was concluded that physicians with higher titles and career levels are more likely to influence career choices when they receive positive feedback originating from their patients and surroundings. For the study of physician-patient interactions during treatment follow-up, we only concluded a positive influence. Overall, our findings suggest that treatment process expectations are an important factor influencing patient satisfaction and evaluation scores. On telemedicine platforms, healthcare organizations should pay attention to patients' treatment process expectations and take steps to meet or exceed them in order to improve patient satisfaction and evaluation scores. At the same time, healthcare organizations also need to focus on the professionalism of physicians and the quality of online services, as well as other influencing factors such as the convenience of treatment modalities and patient compliance. 6.3 Practical implications: First, we found that patients are more inclined to believe information from sources that match their beliefs and expectations, and that patients' expectations and beliefs will greatly influence medical outcomes. Therefore, patients should place themselves in a rational perspective when choosing medical information, rather than believing that information that matches their expectations and beliefs is helpful. Patients who are confident in the treatment process are more likely to experience faster recovery and to be satisfied with the overall healthcare experience, and this result provides physicians with new ideas for the treatment process. Proposing a treatment plan that is consistent with the patient's beliefs from the patient's point of view can be more acceptable to the patient, and raising the patient's expectations and understanding the patient's personalized needs through verbal and behavioral means can significantly Secondly, the doctor's expectations of the patient are more acceptable to the patient. Second, physician expectations of patients can lead to the creation of multifaceted congruence. When a physician confers an expectation on a patient, the patient is more likely to make consistent choices, choosing to accept consistent information, and will then realize that this coincides with the theory of multifaceted congruence in the Pygmalion effect Similarly, physicians are able to optimize healthcare teamwork, communication and shared expectations between physicians and other healthcare professionals by intentionally guiding the patient in the direction of established treatments in the course of their communication with the patient, which An environment more conducive to the patient's recovery can be created. This will lead to a positive effect on the patient's own process of internalizing the information. Finally, by analyzing the sentiment of user ratings on healthcare platforms, we found that the Pygmalion effect will lead to higher and higher ratings for good doctors, which in turn reverses the effect. It also gives each of us a new way of thinking when choosing a doctor; focusing on doctors with high ratings may not make our situation better, but instead focusing on healthcare professionals who match and are more able to help us with our condition. During follow-up, physicians should perpetuate positive expectations and improve patient self-efficacy, and this study can be used to improve the training and education of healthcare professionals. Developing awareness of positive expectations for patients and effective communication skills among physicians and other healthcare practitioners can help improve the quality of healthcare services. It is also important for patients to maintain as high a level of self-efficacy and adherence as possible during the follow-up phase after treatment is completed, and the interplay between the two will maximize the effectiveness of the follow-up process. 7 Conclusion Potential healthcare customers can find lower search expenses by participating in online health communities. In addition, patients and doctors find the online healthcare services market appealing due to the convenience of advocacy, counseling, and treatment as well as the platforms' profitability. On the other hand, not much study has been done on how effective internet healthcare can be for patients in terms of therapy. It would be beneficial to research the patient treatment procedure. It will shed light on how the patient treatment procedure affects the Pygmalion effect. We find that patients' information choices, treatment expectations, self-efficacy, and physicians' expectations may influence patient treatment as well as follow-up using a longitudinal dataset from three major healthcare websites and machine learning techniques. Simultaneously, this may influence modifications in the ratings of physicians. Specifically, Patients with more complicated illnesses are more susceptible to this impact, which should be avoided. Our study explains the importance of physician traits and website features, and it offers important insights into how patients choose doctors and how doctors treat patients. Related studies on functionality, service surveys, and utilization in the online healthcare industry may be able to draw attention to and support the Pygmalion effect. Declarations CRediT authorship contribution statement Xin Shen: Conceptualization, Methodology, Formal analysis, Writing –review & editing, Writing – original draft, Data curation. Yulin Yan: Conceptualization, Methodology, Writing – review & editing, Software, Data curation, Investigation. Huikang Liu: Validation, Resources. Conceptualization, Supervision, Writing – review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability A portion of the data that support the findings of this study are available from National Population Health Data Center at https://www.ncmi.cn/ and The other part comes from Large online health sites.( https://www.webmd.com/,https://www.haodf.com/ , http://www.amazonaws.cn,https://www.practo.com/ ) Ethics approval and consent to participate Ethical and informed consent for data used This article does not contain any studies with human participants or animals performed by the author. 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Supplementary Files Table2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 27 May, 2024 Submission checks completed at journal 20 May, 2024 First submitted to journal 20 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4449255","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307333181,"identity":"cb8f33cf-f12e-4114-9921-e47b36f4893e","order_by":0,"name":"Xin 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15:41:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1486128,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4449255/v1/faad0119a0fc2950fc07006b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Do people only believe what they want to believe? An empirical analysis of the Pygmalion effect in telemedicine platforms based on linear regression algorithms","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e A researcher has done such an experiment: \u003cspan\u003e$\u003c/span\u003e10,000 worth of red wine labeled with \u003cspan\u003e$\u003c/span\u003e500, and at the same time to \u003cspan\u003e$\u003c/span\u003e10,000 red wine labeled with \u003cspan\u003e$\u003c/span\u003e500, to the test persons to taste and ask them to evaluate, the results of the results through the aggregation of more than 95% of the participants think that \u003cspan\u003e$\u003c/span\u003e10,000 labeled red wine tastes more appealing to them. Despite the randomized nature of the experiment, it still illustrates the Pygmalion effect, a theme that this paper seeks to explore. With the development of the web3, the use of third-party information platforms to integrate information from multiple websites at the same time is increasingly appealing to people with different needs. Some of the more established applications include the online healthcare platforms explored in detail in today's paper\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].It is widely known that medical decision-making, despite being as humanized and precise as possible nowadays, also still suffers from decision-making bias caused by a variety of reasons, and that medical decision-making is a cumbersome process influenced by the complex relationships between patients, doctors, and healthcare professionals. Into this delicate balance creeps the Pygmalion effect, described in psychology, which injects a dash of complexity into decision-making. This effect reveals that when faced with conflicting choices or information, people tend to choose the option that is more familiar or consistent with their existing beliefs. In healthcare, this phenomenon may have far-reaching implications for the decision-making process of both patients and physicians. More obvious and easy to study are online healthcare platforms such as webmd, practo, good doctors etc. Enabling physicians to fully utilize the use of their spare time and medical expertise to serve remote patients enables patients to access online healthcare services through online platforms[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. People can access health information and services or manage their health status by consulting with remote physicians in hospitals across the country[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The recent changes in healthcare choices in online health communities are all a widespread and ongoing phenomenon, as patients have limited receptivity to reliable information, and patients are more likely to be biased due to their lack of specialized medical knowledge. One of the most common scenarios is that patients tend to be more inclined to choose doctors who match their pre-existing beliefs or expectations, as well as epitomizing the choice of treatment options, and doctor-patient communication. This is a more common manifestation of the Pygmalion effect. On online healthcare platforms, physicians can present their medical knowledge and treatment information to patients who visit the online platform [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. At the same time, communication between doctors and patients is posted on the healthcare platform network. There is a wealth of valuable information that can help build knowledge-based online support for patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, it is necessary for patients to build mental anticipation and preparedness to recognize this effect when reviewing the large amount of health information displayed to understand the disease. This not only has important implications for patient safety, but is also a determinant of the efficient and rational use of healthcare resouces [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It also has greater significance in assessing the quality of service and electronic word-of-mouth (eWOM) of physicians. This study contributes to the research community in several ways. First, this study is one of the first to investigate the Matthew effect in the online healthcare community by investigating the impact of online ratings on physician platform revenue. While previous studies have investigated the relationship between online ratings and product sales and seller revenues in the retail industry [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], the findings may not be applicable to services in the health industry. Health services, in which there are inherent differences and class subjectivity in service experiences, service content and quality are even more directly related to patient health. Unlike retail operations, little is known about the Marion impact of online ratings on health care. Therefore, this study explores this gap from the perspective of the Marion effect. Second, we provide insight into the relationship between the Marion effect, treatment resource selection, and physician diagnostic quality by examining and validating the moderating role of physician attributes through a sequence of hypotheses followed by arguments. Recognizing the presence of the Marion effect in medical decision-making helps healthcare professionals to assess the patient's situation more comprehensively and provide more balanced and objective information to facilitate more rational and integrated medical decision-making. At the same time, patients can be more open to different perspectives and treatment options for better medical outcomes, providing a clearer understanding of the intersection of psychology and medicine and enhancing communication with physicians. The remainder of this study is as follows: in Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we review the literature in more details. In Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we begin by preparing to accept the development of the hypotheses. Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes the research methodology. Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses the key findings, theoretical implications, practical implications and limitations of the study. Finally, in Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e6\u003c/span\u003e, we summarize the results of this study.\u003c/p\u003e"},{"header":"2. literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Related research\u003c/h2\u003e \u003cp\u003eThe Pygmalion effect refers to a psychological phenomenon in which beliefs or expectations that are expected to lead to the occurrence of an outcome make that outcome more likely to occur .In a healthcare setting, the attitudes, words, or actions of doctors, nurses, or other healthcare professionals toward patients or the care they provide may affect patients' expectations, which in turn may affect treatment outcomes or the recovery process.The Pygmalion effect has attracted a great deal of research effort, part of which seeks to gain insights into the interactions between patients, physicians, and the healthcare environment in order to optimize medical practice and improve patient outcomes. For example, patients with suspected COVID \u0026minus;\u0026thinsp;19 tend to exhibit symptoms of depression, anxiety, and irritability during quarantine, and psychological care based on the Pygmalion effect can help alleviate their negative emotions [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].In examining the Pygmalion effect in nursing home caregiver expectations on patient prognosis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].The application of the Pygmalion effect intervention model to elderly patients with COPD and lung infections promotes clinical regression, improves their pulmonary function and quality of life, and thus enhances satisfaction with care[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]It is believed that the Pygmalion effect, when applied to breast cancer patients during surgery, can improve their psychological resilience, enhance their belief in healing, effectively improve their treatment compliance, and J.Y.Kim et al.lead to a rapid recovery from the disease, which is worthy of popularization and application [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A systematic evaluation revealed different domains of doctor-patient relationship and communication with convincing effects on different objective and subjective health outcomes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is believed that the Pygmalion effect intervention is effective in improving respiratory exercise adherence and lung function levels in patients undergoing surgery for esophageal cancer, enhancing psychological resilience and self-efficacy, and predisposing them to face and deal with problems in a positive attitude and manner [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn online healthcare, there is an important influence of the Pygmalion effect. For example, a physician's or nurse's cordial attitude, professional explanations, or advice provided to a patient during a teleconsultation may enhance the patient's confidence in treatment, thereby promoting better adherence to medical advice and improving treatment outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In addition, the design and functionality of online healthcare platforms may also influence patients' expectations, e.g. user-friendly interfaces and operational processes may enhance patients' confidence in healthcare services, thereby improving treatment outcomes. More obvious and easy to research are online healthcare platforms such as webmd, practo, good doctors, etc. These platforms allow doctors to make the best use of utilizing their spare time and medical expertise to provide services to remote patients, enabling patients to experience appropriate services through online platforms [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. People can access health information and services or manage their health conditions by consulting remote doctors in hospitals across the country [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. On online healthcare platforms, doctors can present their medical knowledge and treatment information to patients who visit the online platform. At the same time, the communication between doctors and patients also posts that there is a large amount of valuable information on the network of healthcare platforms that can help build knowledge-based online support for patients [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This paper focuses on how people's expectations and beliefs about healthcare information and services affect their actual health outcomes. The most relevant literature to this study is the empirical analysis of the Matthew effect on online healthcare communities [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. For example, Zhuo et al. explains that online physician ratings from third-party platforms are important for patient decisions in this particular healthcare context.\u003c/p\u003e \u003cp\u003eTherefore, understanding and capitalizing on the Pygmalion effect is important in online healthcare to enhance patient confidence and treatment outcomes through the provision of high-quality healthcare services and the design of user-friendly platforms.\u003c/p\u003e \u003cp\u003eThis study reveals the existence of the Pygmalion effect on healthcare platforms, emphasizing the actual impact of expectations and beliefs between physicians and patients on healthcare outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Online service and interactions in medical information\u003c/h2\u003e \u003cp\u003eOnline service has been widely studied as a new type of information access means, and in the access to medical information, online platforms provide more channels and ways [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This is also an expression of interactions, and we have done a research on the common behaviors when accessing medical information. People often ask people they know around them, which is an extremely common and cheap way to obtain information, but people, as emotional animals, often filter the information and add a certain subjective color to express it to the other side, which leads to the information obtained through this channel is not widely applicable and a certain degree of deception. The Marion effect is manifested in the fact that information obtained from people who know the person well can always bring great psychological comfort, especially to those who are ill, but this information also influences the patient's rational judgment to some extent, thus delaying the illness [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This information asymmetry and the main theoretical framework, interaction theory, which will be presented below, are the underlying logical foundations that lead to the Pygmalion effect. Search engine queries e.g. through search engines such as Google, Baidu, etc., typing in symptoms, disease names or other relevant medical keywords in order to obtain relevant information is also a favored way [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. And as mentioned above, the maturity of web 2.0 has also brought medical website browsing: visiting professional medical websites and health information platforms, such as WebMD, Mayo Clinic, etc., for medical advice and professional opinions into the public eye [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This also includes the platforms that will be the focus of this paper's investigation. Second, social media participation allows for the sharing of personal experiences, reading health blogs and articles provides access to professional information on health and medical care, medical literature searches provide access to more in-depth medical knowledge and the latest research findings, and mobile app use and participation in online Q\u0026amp;A platforms allow for a more personalized and self-directed treatment process [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. All of these behaviors reflect the diverse access to healthcare information in modern society, leading to a more comprehensive understanding and management of one's health [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, it is important to note that this process is influenced by the psychological effects of the individual patient as well as the environment. Individual psychological processes are subject to two types of social influences: normative influences and informational influences [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Normative Influence Under affective influence, individuals process information and make decisions based on heuristic cues Informational Influence, individuals expend cognitive effort to process information and make decisions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As a result, normative affect requires less cognitive effort from the individual than it does from the individual's perceived information relationships [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. There is then 1. Information Overload: The internet provides a large amount of medical information, which can lead to patients feeling overwhelmed. Information overload may make it difficult to sift through and make sense of information, thus affecting the quality of decision-making.2 Confirmation Bias: People tend to be more receptive to information that is consistent with their existing beliefs or opinions, while ignoring or being skeptical of information that contradicts them. This may lead patients to selectively accept medical information that meets their expectations. 3. Social Comparison: People may compare their medical experiences with those of others, which may trigger anxiety or expectations. The social comparison effect may cause patients to feel uneasy about their health status or to have high expectations of particular treatments [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Unfortunately, these are just three representative psychological effects, along with the physiologic support for the Pygmalion effect in this context. A similar psychological effect related to the Marion effect is the Illusory Correlation effect, in which people may sometimes be inclined to establish false correlations between different pieces of medical information rather than assessing the credibility of the information based on scientific evidence. This can lead to an inaccurate understanding of the information, and in fact, this can be said to have quite a bit in common with the Marion effect, both of which are flaws in information selection due to mental bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Theoretical framework\u003c/h2\u003e \u003cp\u003eIn the context of online health communities, when patients make decisions based on the influence of information (consulting a doctor), they need to spend a lot of effort on information. In the case of online healthcare platforms, for example, information is provided by the website, such as the doctor's personal characteristics or the doctor's past consultation records [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. When patients make decisions based on the corresponding influences, they only need heuristic cues, such as the doctor's ratings, honorary titles, and reviews from cured people. Once repeated contact with familiar sources has been made, interactions manifest themselves. Interaction theory, which is widely used and embodied across multiple schools of thought, can find a range of related theories and ideas behind it: one of them, social cognitive theory, will brilliantly help us understand the Pygmalion effect in medical effects. Patients are more likely to adopt trusted, familiar sources of information, and communication between family and friends as well as title-rich specialists can have a big cognitive change on them, even if it may not be the optimal treatment option.\u003c/p\u003e \u003cp\u003eAccording to interaction theory, people's attitudes and preferences are formed through interactions between individuals and external stimuli. This interaction includes repeated exposure of people to something, as well as cognitive and emotional processing within the individual. The Marion effect can be viewed as an interaction between an external stimulus (exposure to something) and an individual's internal mental processes (emotional or cognitive processing). In the Marion effect, repeated exposure to something increases familiarity with it, which in turn decreases its unfamiliarity. This reduced unfamiliarity triggers a more positive emotional response. In other words, when we are repeatedly exposed to something, the brain is more likely to process the information because they become more familiar, which causes us to feel more positive emotions towards them [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. From an interaction perspective, this increased familiarity may affect the way an individual cognitively processes that thing, such as recognizing it more quickly, evaluating it more positively, or reacting to it more happily. This change in cognitive processing may lead to a more positive affective experience, which in turn enhances favorable feelings towards the thing. Thus, the Marion effect can be viewed as an interaction between an external stimulus (exposure to something) and an individual's internal affective or cognitive processing, leading to more positive attitudes and preferences towards things. In a more subdivided direction - social cognitive theory - people's behavior and attitudes are influenced by their observations and evaluations of the behavior and attitudes of others [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The Pygmalion effect will not only influence the patient's choices, but will likewise have a change in the physician's choice and evaluation of the platform, thus influencing the patient's choice and evaluation of the treatment platform [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Theoretical background and hypothesis development","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Efficacy modeling\u003c/h2\u003e \u003cp\u003eBased on previous discussions, this study proposes the research model shown in Figure.1.\u003c/p\u003e \u003cp\u003eAs the interaction theory and the social cognitive theory under it are the theoretical basis of this paper to study the Marion effect, the theory emphasizes that people interact and influence each other with their surroundings thus forming qualitative changes. The two most important factors as independent variables are separated separately for Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, namely 'individual' and 'culture and environment', which as parts constitute the key variables of the interaction theory, and at the same time as a whole for the Pygmalion effect to be Influence. This allows for the following precise conceptualization and definition of the effect. The Pygmalion effect, also known as the self-fulfilling prophecy, is a psychological phenomenon in which the expectations, beliefs, or anticipations of others have an impact on an individual's behavior and performance. In psychology, the Pygmalion effect describes how people tend to conform to what others expect of them [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. If a person is told or believes that they have a certain ability, trait, or potential, they are more likely to exhibit behavior that conforms to that expectation. This expectation may be communicated non-verbally or verbally and can come from teachers, parents, employers or other authority figures. The central idea of this effect is that the beliefs or expectations of others may influence individuals' self-perceptions and behaviors, thereby prompting them to exhibit traits or behaviors consistent with those expectations. This phenomenon has significant applications and implications in a variety of fields, including education, workplace environments, leadership, healthcare, and interpersonal relationships. Some of the important components are as follows.\u003c/p\u003e \u003cp\u003e1. Expectations and Expectations: the expectations and anticipations that others have of a person or group. This may be based on beliefs about some specific traits, abilities, or behaviors, usually expressed nonverbally or verbally.2. Self-fulfilling prophecies: People's behaviors and performances may be influenced by the expectations others have of them, which can lead to those expectations becoming reality. If people are told they have a certain ability or trait, they are more likely to demonstrate it.3. Social interaction and communication: This phenomenon is usually realized through social interaction, nonverbal and verbal communication. The expectations of others may be conveyed to the individual through body language, verbal expression, or other means of communication, thereby influencing his or her behavior and performance.4. Individual Performance and Behavior: People may be inclined to conform to the expectations that others have of them. They are more likely to exhibit positive behaviors or abilities if they perceive positive expectations from others.\u003c/p\u003e \u003cp\u003eThe theoretical basis for this effect lies in the emphasis on the influence of social expectations on individuals, which can change their behavior and performance, making expectations a self-fulfilling prophecy. This concept has important applications for areas such as education, leadership, and interpersonal relationships.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Decision quality modeling\u003c/h2\u003e \u003cp\u003eThe most important part of the healthcare process, whether online or offline, is the choice of treatment, and due to the intervention of the Marion effect, patients will be more inclined to choose doctors who match their pre-existing beliefs or expectations in this important process. It is based on past experience, tradition, or a match with the personal traits of the doctor. When patients feel that a physician matches their expectations, they are more likely to build trust and satisfaction with the physician [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, when physicians are making decisions, this effect may lead them to make different medical decisions in similar cases, which will increase the risk for patients. In addition to this, the effect on the physicians themselves should not be underestimated and will be reflected in, for example, rankings, scoring, and follow-up of patients, among other things. Decision quality is often viewed as how close the decision maker's implementation effect is compared to the performance of the actual goal [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It may affect the satisfaction of the initiator of the problem (here the patient, later replaced by patient) and further influence the patient's behavioral intentions such as acceptance and rejection [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. For the patient's satisfaction with the treatment decisions proposed by the physician here, we refer to this effect as service quality, which is generally designated as a multidimensional and hierarchical concept [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Many researchers like Gummerus et.al have focused on the measurement of service quality. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Thay used a two-dimensional space (technical quality and functional quality), where technical quality relates to the information that the patient receives from the physician, while functional quality relates to the way in which decisions are provided. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] constructed service models that measure patient perceptions of treatment decisions. Regarding healthcare services, [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Donabedian et al. used both technical quality and interpersonal quality to measure the quality of healthcare services from different perspectives. The former quality of service refers to the application of medical science and technology in healthcare, while the latter refers to the communication between physicians and patients. McCulloch et al.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] also summarized the conclusion - there are three dimensions of quality (system quality, information quality and interaction quality) in healthcare services. so it is not difficult to observe that a good experience of interacting with a doctor can add value in both traditional offline and online healthcare services, but this behavior is greatly affected by the Marion effect. In general, the perceived quality of information affects the patient's diagnostic intent. Other patient-rated service quality influences patient decisions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].Patients can evaluate the caliber of a doctor's care with the aid of the wealth of online healthcare data found on online healthcare platforms, including online patient numbers, online reviews, and online medical records. Patients will use this information to help them make an ultimate decision on which doctor to see and how cooperative they are during the course of therapy. Our study will use text mining and sentiment analysis in natural language processing to replicate the above procedure based on prior research; the examination of these variables will be covered in a later section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Research hypotheses\u003c/h2\u003e \u003cp\u003eIn this section we continue to use the research model in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e to formulate our hypotheses. First, we investigated the main influence of effect on patient's choice of doctor and treatment outcome. In order to rationalize this relationship, we considered some important factors that can moderate the effect of patient's choice of doctor. More specifically, this study investigated the following factors: (1) treatment expectations from the patient, (2) the patient's sources of information, (3) the physician's choice of treatment modality, (4) the patient's affective bias, and (5) is the level of the physician's academic title. Investigating these moderators will help reflect the Pygmalion effect of physicians and online healthcare platforms, which is often based on physician characteristics. A comprehensive understanding of how these moderators influence patients' choices of physicians is important for these platforms to accurately estimate treatment outcomes and provides better guidance for designing appropriate online search matching strategies. Existing literature has long documented online ratings of products or services as key heuristic cues in the consumer decision support process. In the context of the online healthcare industry, upon learning that they have been diagnosed with a particular disease, most patients will be the first to inform their family members, relatives, or even friends for advice and assistance. On online healthcare platforms, patients and their relatives can easily access many relevant medical information, such as relevant patient ratings, review testimonials, and doctor specialization modules. The content showing this doctor will be on the personal page viewed by the patient, so that potential patients can learn about the doctor. Thus, both of these approaches are considered as patients' access to medical information. When patients or their family members perform a search, the portal will always recommend the higher rated doctors first for doctors based on vague conditions related to diseases in the online health community. On the one hand, sorting doctors' ratings in descending order can help patients quickly choose the right doctor because these highly rated doctors tend to be leaders in disease-specific related fields. However, this sorting method greatly reduces the likelihood that patients will consult highly rated and other rated physicians, and the positive bias in patients' ratings (patients' tendency to choose physicians corresponding to positive sources of information) may increase the ratings of these physicians thereby exacerbating this effect. Therefore, we hypothesize that when choosing a physician, patients are more likely to accept a physician who matches the information they themselves have acquired and ignore or reject information that contradicts it. So it also is to an overall extent, this effect exacerbates the vicious circle. It is therefore reasonable to hypothesize that patients are more inclined to trust sources of information with a high degree of cordiality, and that when such information is received to a certain extent, the more likely it is that patients will make choices and judgments that are broadly consistent with it. That is, the Pygmalion effect will continue to operate when more belief-consistent information is available. Based on the above discussion, we expect that the number of sources of information that patients trust and the quality of such information will positively modulate the strength of the Pygmalion effect. Patients' judgments and decisions will be altered as a result. Based on the above discussion, we formulate the following hypotheses: H1. Patients are more likely to receive medical information that is consistent with existing beliefs or expectations, especially in the case of serious illnesses, and the quality and quantity of such information will influence the strength of the Pygmalion effect. Of course, after obtaining information patients will mentally generate a threshold of psychological expectations for this information, and when enduring psychological emotions higher than this value, patients will maintain a consistently positive therapeutic attitude and psychology; conversely, below this level patients will be in the midst of negative psychological cues for a long period of time. Discussing the presence of the former situation, patients have positive expectations of the doctor, they are more likely to actively cooperate with the treatment, which in turn has a better impetus to the results. Similarly, the patient's state of mind is altered from time to time by the interactions of the surrounding environment (Interaction Theory) during the process, which will cause unpredictable fluctuations in treatment outcomes. This is also a good evidence of the direct effect of the Pygmalion effect. Inspired by this, we have also proposed the resonance effect, which, although already in place, is equally applicable in the medical field, where resonating with a treatment program is more likely to have a positive impact on treatment outcomes. Similar to the generation of empathy, when a physician expresses positive expectations for a patient, the patient may be more inclined to trust this physician and to choose treatments that are consistent with the physician's expectations. Responding to the physician's expectations in this way is due to the fact that patients tend to seek guidance in the physician's expertise and advice and want a treatment plan that the physician endorses. This contributes to a positive patient-physician relationship. The patient may feel concerned and supported by the physician and thus be more motivated to actively participate in the treatment process. These are two aspects of the principle of consistency, on which we can expand to get: Consistency of Information Sources: Patients may be influenced by multiple sources of information when making healthcare choices, such as healthcare professionals, family members, friends, the media, or the Internet. If these sources of information consistently emphasize the strengths or effectiveness of a particular doctor or treatment modality, the patient is more likely to choose an option that is consistent with this consistency. Social Expectations and Choice Consistency: Social expectations and anticipation of medical choices may also influence patient decision-making. If society generally agrees that a certain medical option is better, patients may be more likely to choose an option that is consistent with this social expectation in order to avoid social pressure or to gain social acceptance. Due to the Pygmalion effect, the consistency of the patient's choice of treatment may improve treatment outcomes to some extent. Positive attitudes and feelings of trust in patients may influence physiological and psychological processes and have a positive impact on recovery. Therefore, we formulate the following hypotheses: H2. Patient expectations, mindset, and empathy with the treatment plan will greatly influence treatment outcomes. H3. Physician expectations of patients play a positive role in the emergence of the principle of multifaceted consistency. We then consider the moderating effect of different sources of expectations on physicians' career choices. The gap between healthcare platforms and physician expectations. Prior literature on social structure suggests that socially advantaged individuals should have a competitive advantage that can be used to obtain more resources [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].Specifically, members of a better organization tend to have better control over resources. For example, Jiang et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] noted how cities control more healthcare knowledge and resources as this study evolved. As a result, a good hospital may have better doctors, more advanced equipment, and a better healthcare environment. Patients also expect to choose doctors belonging to higher level hospitals or platforms in their decision making process [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].Patients may be more inclined to choose doctors if they expect more from online platforms, believing that the services they provide are more specialized or more trustworthy. As a result, high level hospitals and platforms will help physicians gain more advantage over the competition. Specifically, when patients choose to consult physicians affiliated with higher-level hospitals, the conflict between patients' expectations and actual services may appear to decrease due to the higher actual quality of care provided by these physicians. Similarly, physicians' own expectations of this kind are reduced when they are attached to a higher-ranking platform. Conversely, when physicians have positive expectations about their career prospects, they believe that they can increase their visibility, professional prestige, and even expand their career opportunities by participating in online platforms. This will lead to a change in the physician's career outlook. Therefore, we expect that when patients perceive and respond positively to a physician's positive expectations, this may motivate physicians to become more engaged and achieve greater success in their careers [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].This leads to the following hypothesis: H4. Expectations from platforms, patients, and physicians themselves have an impact on physicians' career prospects. This study further considered whether the Marion effect moderates the impact of online physician ratings on treatment outcomes. The Chinese healthcare market has a strict hierarchy. In China, doctors have two titles: clinical title2 and academic title.3 The level of clinical title reflects a doctor's medical experience, while the level of academic title reflects a doctor's academic level. In general, patients are more willing to trust doctors with senior titles. In other words, patients will prioritize doctors with higher titles for online or offline consultations. In addition, for a doctor with a higher clinical and academic title, he/she has a higher level of medical proficiency and experience in treatment and counseling. Online consultations provided by these physicians are more likely to meet patients' expectations. Thus, the influence of ratings on patients' choice of physician and their subsequent treatment outcomes may be due to the Pygmalion effect. The effect of patient choice on physician ratings may be greater for physicians with higher clinical and academic titles. Therefore, we expected that the level of a physician's clinical title and academic title would positively moderate the positive correlation between physician ratings and patient preference choices. This leads to the following hypotheses: H5. The Pygmalion effect will lead to higher and higher ratings of good physicians, which in turn reverses the effect.H6. Physician expectations, patient self-efficacy, and adherence during subsequent physician-to-patient follow-up will have a two-sided effect on recovery outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Data description and summary statistics","content":"\u003cp\u003eWe conducted independent text mining to obtain data from China's largest healthcare portal, \"Good Doctor,\" as well as two large online healthcare platforms in Europe and the United States, webmd and practo, to study the impact of the Pygmalion effect in healthcare.\" Good Doctor\" is an independent healthcare platform connecting patients and doctors, with more than 240,000 registered doctors covering about 10,000 regular hospitals. Doctors' basic information (e.g., affiliated hospitals, areas of specialization, clinical titles, academic titles, etc.) can be displayed on the platform's personal page. Through the platform, patients can find suitable doctors for consultation and diagnosis. Consultations can be conducted online through the interface provided by the platform or over the phone, for which a fee is usually required. After the consultation, the patient or his/her relatives can vote for the doctor through the platform's service interface, or even write comments such as thank you letters to the doctor, or buy virtual gifts for the doctor. We developed a python-based crawler program to collect data from these three websites. To ensure a fair sample, we collected data from all doctors. We then randomly selected 5000 doctors over an 8-month period (from January 2023 to August 2020) from their personal pages and information about their affiliated hospitals. Our sample was organized by month. The collection process lasted about three days. Therefore, data updates during the capture period are unlikely to interfere with our findings. In addition, for webmd and practo, we used two main approaches: text mining based on big data technology and sentiment analysis based on natural language processing. First, we mined text from webmd pages as well as doctor-patient communication interface pages, and then filtered out information containing emotions obtained through crawling. We left the information related to the patient's emotional expression and then categorized this information according to its source, thus avoiding redundant emotional samples and reducing data errors. We excluded from the sample physicians who did not receive any patient comments or feedback during the study period, as the data from these physicians were not relevant to our current research considerations. The final sample size consisted of 10,243 physicians and 50,625 observations. The sample included physician attributes, healthcare costs, physician-patient communication, ways of consulting physicians, healthcare choices, and physician decision-making. Physician attributes included online ratings, service satisfaction, outcome satisfaction, attitude satisfaction, registration time, total visits, academic title, clinical title, working hospital or online platform, number of patients, number of articles, and number of thank you notes. There are two main sources of information for doctors on the \"Good Doctor\" platform: one is through referrals from close friends, relatives, etc., and the other is through self-knowledge of choosing a doctor. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive statistics of the key variables. We performed large-scale text data mining of the information on the searchable communication pages of these three websites to extract useful information from them. This process involves identifying, extracting, and reasoning about meaningful conversations, sentiments, etc. from the text. Our task focuses on machine learning-based text classification and sentiment analysis, and later on linear regression analysis algorithms based on the above variables. Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the relevant mining statistics for the key variables and the corresponding sentiment analysis results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35861.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45876.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline service satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegistration time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.47E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2484067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5003597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost of treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of articles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of thank-you letters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e468.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"5. Research Methodology","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.1.Linear regression analysis and Modeling of 6 Factors Affecting Patient Evaluation Scores\u003c/h2\u003e \u003cp\u003eIn order to address our first question, the model was constructed by first analyzing (1) treatment cost (2) treatment process expectations (3) total number of visits to the corresponding physician (4) total number of visits to the corresponding physician (5) treatment access (matching expectations as 1) (6) adherence as the independent variable, and treatment efficacy satisfaction, attitudinal satisfaction, and evaluation scores as the dependent variables:\u003c/p\u003e \u003cp\u003e \u003cem\u003eTreatment efficacy satisfaction\u0026thinsp;=\u0026thinsp;a0\u0026thinsp;+\u0026thinsp;a1* (treatment cost)\u0026thinsp;+\u0026thinsp;a₂* (treatment process expectations)\u0026thinsp;+\u0026thinsp;a3* (total number of visits to the corresponding doctor)\u0026thinsp;+\u0026thinsp;a4* (doctor's online rating)\u0026thinsp;+\u0026thinsp;a5* (access to treatment modalities)\u0026thinsp;+\u0026thinsp;a6* (adherence)\u003c/em\u003e (1)\u003c/p\u003e \u003cp\u003e \u003cem\u003eAttitudinal satisfaction\u0026thinsp;=\u0026thinsp;a7\u0026thinsp;+\u0026thinsp;a8* (treatment cost)\u0026thinsp;+\u0026thinsp;a9* (treatment process expectations)\u0026thinsp;+\u0026thinsp;a10* (total number of visits to the corresponding doctor)\u0026thinsp;+\u0026thinsp;a11* (doctor's online rating)\u0026thinsp;+\u0026thinsp;a12* (treatment modality access)\u0026thinsp;+\u0026thinsp;a13* (adherence)\u003c/em\u003e (2)\u003c/p\u003e \u003cp\u003e \u003cem\u003eEvaluation scores\u0026thinsp;=\u0026thinsp;a14\u0026thinsp;+\u0026thinsp;a15* (treatment cost)\u0026thinsp;+\u0026thinsp;a16* (treatment process expectations)\u0026thinsp;+\u0026thinsp;a17* (total number of visits to the corresponding physician)\u0026thinsp;+\u0026thinsp;a18* (physician online rating)\u0026thinsp;+\u0026thinsp;a19* (treatment modality access)\u0026thinsp;+\u0026thinsp;a20* (adherence)\u003c/em\u003e (3)\u003c/p\u003e \u003cp\u003eTreatment Effectiveness Satisfaction Efficacy, Process Expectation, Physician Online Score and Treatment Modality Access have a significant effect on treatment effectiveness satisfaction, and Treatment Cost, Treatment Process Expectation, Corresponding Total Number of Physician Visits, Treatment Modality Access and Adherence have a significant effect on the evaluation scores. Thus, on average, for every percentage point increase in treatment process expectations, the total number of treatment efficacy satisfaction increases by 2.25 percentage points, while the evaluation score increases by 5.20; for every percentage point increase in physician online ratings, the total number of treatment efficacy satisfaction increases by 12.39 percentage points, while the evaluation score increases by 0.97; for every percentage point increase in treatment modality access, the total number of treatment efficacy satisfaction total would increase by 2.10 percentage points, while the evaluation score would increase by 10.13; for each percentage point increase in treatment cost, the evaluation score would increase by 2.60; and for each percentage point increase in adherence, the evaluation score would increase by 4.20.\u003c/p\u003e \u003cp\u003eMeanwhile, the impact factor model with evaluation score as the dependent variable.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSatisfaction with treatment efficacy = -0.58 - (treatment cost)\u0026thinsp;+\u0026thinsp;2.25* (treatment process expectations)\u0026thinsp;+\u0026thinsp;0.64* (total number of visits to the corresponding doctor)\u0026thinsp;+\u0026thinsp;12.39* (doctor's online rating)\u0026thinsp;+\u0026thinsp;2.10* (access to treatment modalities) \u0026minus;\u0026thinsp;0.65* (adherence)\u003c/em\u003e (1)\u003c/p\u003e \u003cp\u003eThe best fit of R2_a\u0026thinsp;=\u0026thinsp;0.983 indicates that the model is statistically significant. Close to this is the model with treatment efficacy satisfaction as the dependent variable.\u003c/p\u003e \u003cp\u003e \u003cem\u003e(Evaluation Score)\u0026thinsp;=\u0026thinsp;3.70\u0026thinsp;+\u0026thinsp;2.60* (Cost of Treatment)\u0026thinsp;+\u0026thinsp;5.20* (Expectation of Treatment Procedure)\u0026thinsp;+\u0026thinsp;1.89* (Corresponding to Total Number of Doctor Visits)\u0026thinsp;+\u0026thinsp;0.97* (Doctor's Online Score)\u0026thinsp;+\u0026thinsp;10.13* (Access to Treatment Modality)\u0026thinsp;+\u0026thinsp;4.20* (Adherence)\u003c/em\u003e (3)\u003c/p\u003e \u003cp\u003eR2_a\u0026thinsp;=\u0026thinsp;0.896, which is also a better fit, and the model with Attitude Satisfaction as a Dependent Variable.\u003c/p\u003e \u003cp\u003e \u003cem\u003e(Attitudinal satisfaction)\u0026thinsp;=\u0026thinsp;1.82\u0026thinsp;+\u0026thinsp;0.66* (treatment cost) \u0026minus;\u0026thinsp;0.09* (treatment process expectations)\u0026thinsp;+\u0026thinsp;0.72* (total number of visits to the corresponding doctor)\u0026thinsp;+\u0026thinsp;1.54* (doctor's online rating) \u0026minus;\u0026thinsp;1.22* (access to treatment modalities) \u0026minus;\u0026thinsp;0.86* (adherence) (2) R2_a\u0026thinsp;=\u0026thinsp;0.0682\u003c/em\u003e,\u003c/p\u003e \u003cp\u003ewhich is not significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of six independent variables, including treatment process expectations, on patients' treatment efficacy satisfaction (1), attitude satisfaction (2), and evaluation scores (3). The numbers in parentheses are the corresponding standardized coefficients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1n(treatment cost)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.292**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.616**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.453***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1n (Total number of corresponding physician visits)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.198*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor's Online Scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.837***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(12.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to treatments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.678**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.043***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(10.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompliance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.275***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.963*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.716***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003er2_a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e932.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote:***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01,**p\u0026thinsp;\u0026lt;\u0026thinsp;0.05,*p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Hypothesis validation and model fit assessment\u003c/h2\u003e \u003cp\u003eIn order to explore the effects of the main independent variables on the dependent variable in different hypotheses, as well as to examine the moderating effects of the moderating variables, the moderating effect analysis was carried out by linear regression, and a linear regression model was constructed with the following model expression:\u003c/p\u003e \u003cp\u003e \u003cem\u003e(target variable)\u0026thinsp;=\u0026thinsp;a21\u0026thinsp;+\u0026thinsp;a22 * (research main independent variable)\u0026thinsp;+\u0026thinsp;a23 * (moderating factor)\u0026thinsp;+\u0026thinsp;a24 (interaction factor)\u0026thinsp;+\u0026thinsp;a25 (control variable 1) ...\u0026thinsp;+\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;ε (4)\u003c/em\u003e \u003c/p\u003e \u003cp\u003ewhere the target variable and the research main independent variable need to be based on the needs of different hypotheses; moderating factor is the non-negligible significance in the hypothesis affecting the target variable. The interaction factor is the part of the interaction between the main independent variable and the moderating variable that affects the target variable, a21 is the intercept term, a22, a23, a24, a24... are the model coefficients, \u0026micro; denotes the capture of unobserved individual-specific effects, and ε it denotes the residual random error term.\u003c/p\u003e \u003cp\u003eIn order to test hypothesis one, after centering the data of different variables and detecting multiple covariance, we used the patient treatment process expectation on the telemedicine platform as the independent variable and the total number of visits to the corresponding doctors as the target variable. Moderating effect analysis was performed using linear regression, with the moderating factor being access to treatment modalities. At the same time, considering other possible influences, we controlled for the following variables: (1) adherence (2) physician online scores [note the presence or absence of a high correlation between these variables (Pearson\u0026thinsp;=\u0026thinsp;0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the need to use both variables as covariates if they are present].\u003c/p\u003e \u003ctable id=\"Tab4\" border=\"1\" style=\"margin-right: calc(0%); width: 100%;\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of the moderating effect of Zscore (treatment modality access) on Zscore (treatment process expectations) on Zscore (corresponding to total physician visits).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 31.5961%;\"\u003e\n \u003cp\u003evariant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.8177%;\"\u003e\n \u003cp\u003ecoeff\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003ese\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.9643%;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 31.5961%;\"\u003e\n \u003cp\u003e(constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8177%;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.9643%;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 31.5961%;\"\u003e\n \u003cp\u003eZscore (Doctor\u0026apos;s Online Scores)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8177%;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.9643%;\"\u003e\n \u003cp\u003e2.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 31.5961%;\"\u003e\n \u003cp\u003eZscore(Treatment costs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8177%;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.9643%;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 31.5961%;\"\u003e\n \u003cp\u003eZscore(Treatment Process Expectations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8177%;\"\u003e\n \u003cp\u003e-0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.9643%;\"\u003e\n \u003cp\u003e-4.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 31.5961%;\"\u003e\n \u003cp\u003eZscore(Access to treatments)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8177%;\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.9643%;\"\u003e\n \u003cp\u003e-0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 31.5961%;\"\u003e\n \u003cp\u003eExpectations and approaches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8177%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.9643%;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.0113%;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 31.5961%;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" style=\"width: 25.878%;\"\u003e\n \u003cp\u003e0.536d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 31.5961%;\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" style=\"width: 25.878%;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 31.5961%;\"\u003e\n \u003cp\u003eDurbin Watson.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" style=\"width: 25.878%;\"\u003e\n \u003cp\u003e1.595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAccording to the results of Table IV, it can be seen that treatment process expectation has a significant positive predictive effect on the total number of physician visits (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001),while the moderating variable treatment modality access has no significant positive predictive effect, and the interaction variable of the two is not significant at the level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, which indicates that there is no moderating effect of treatment modality access on the relationship between the treatment process expectation and the total number of physician visits. Substituting (4) indicates a linear regression model as:\u003c/p\u003e \u003cp\u003e \u003cem\u003e(corresponding to the total number of physician visits)\u0026thinsp;=\u0026thinsp;0.087\u0026thinsp;+\u0026thinsp;0.092 * (treatment process expectations)\u0026thinsp;+\u0026thinsp;0.089* (treatment modality access)\u0026thinsp;+\u0026thinsp;0.091 (interaction factors)\u0026thinsp;+\u0026thinsp;0.091 (physician online ratings)\u0026thinsp;+\u0026thinsp;0.089 (cost of treatment $) + \u0026micro;\u0026thinsp;+\u0026thinsp;ε\u003c/em\u003e (5)\u003c/p\u003e \u003cp\u003ewhere \u0026micro; denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive, R2\u0026thinsp;=\u0026thinsp;0.288, and the model fit is fair and does not demonstrate the presence of moderating variables.\u003c/p\u003e \u003cp\u003eIt is noteworthy that the actual line map is affected by the coefficients provided in the model, which are calculated based on the actual data. Here, we will simplify the model and consider only the basic linear relationship between the variables, not including the random error terms༂\u0026micro;༂ and༂ε༂, to facilitate visualization. The figure below shows the effect of the treatment process expectation on the total number of corresponding physician visits, and examines the role of treatment modality access as a regulatory variable in this relationship.\u003c/p\u003e \u003cp\u003eThe graph illustrates the effect of treatment expectation on the number of doctor visits, considering both scenarios with and without the interaction term involving the method of obtaining treatment as a moderating variable.\u003c/p\u003e \u003cp\u003eThe dashed line represents the relationship without considering the interaction between treatment expectation and the method of obtaining treatment, while the solid red line includes this interaction.\u003c/p\u003e \u003cp\u003eAs shown, the inclusion of the interaction term modifies the slope of the relationship, suggesting that the effect of treatment expectation on doctor visits is indeed influenced by how patients access treatment methods.\u003c/p\u003e \u003cp\u003eIn summary, the higher the expectations of the treatment process, the higher the total number of physician visits, and the access to treatment modalities has a smaller effect on the total number of physician visits, and Hypothesis I is supported.\u003c/p\u003e \u003cp\u003eIn order to test hypothesis two, \"patients' expectations, mindset, and empathy with the treatment program will greatly affect the treatment effect,\" we conducted a moderating effect analysis using treatment process expectations as the independent variable and satisfaction with governance effectiveness as the target variable, while examining the moderating effect of adherence and controlling for the following variables:\u003c/p\u003e \u003cp\u003e(1) total number of visits to the corresponding doctor (2) attitude satisfaction (3) treatment cost (4) doctor's online rating. A linear regression model was constructed to explore the effect of treatment process expectations on satisfaction with governance effectiveness. The results of the analysis are presented below:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of the moderating effect of Zscore (adherence) on Zscore (treatment process expectations) on Zscore (satisfaction with governance effectiveness)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoeff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Doctor's online score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(treatment cost \u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(total number of doctor visits)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(attitude satisfaction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Expectation\u003c/p\u003e \u003cp\u003eof treatment process)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Adherence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectations\u003c/p\u003e \u003cp\u003eand compliance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.984d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurbin Watson.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eBased on the results of the analysis, it can be seen that the positive predictive effect of treatment process expectations on satisfaction with governance efficacy is not significant (P\u0026thinsp;\u0026ge;\u0026thinsp;0.01),while the moderating variable of adherence has a significant positive predictive effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the interaction variable of the two is not significant at the level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, which indicates that there is no moderating effect of adherence between treatment process expectations and satisfaction with governance efficacy. Substituting (3) indicated a linear regression model as:\u003c/p\u003e \u003cp\u003e \u003cem\u003e(Satisfaction with governance efficacy)\u0026thinsp;=\u0026thinsp;0.019\u0026thinsp;+\u0026thinsp;0.026 * (Treatment process expectations)\u0026thinsp;+\u0026thinsp;0.043* (Adherence)\u0026thinsp;+\u0026thinsp;0.019 (Interaction factors)\u0026thinsp;+\u0026thinsp;0.043 (Physician online ratings)\u0026thinsp;+\u0026thinsp;0.019 (Cost of treatment $)\u0026thinsp;+\u0026thinsp;0.023 (Corresponding to the total number of visits to the physician)\u0026thinsp;+\u0026thinsp;0.027 (Attitudinal satisfaction) + \u0026micro;\u0026thinsp;+\u0026thinsp;ε\u003c/em\u003e (6)\u003c/p\u003e \u003cp\u003ewhere \u0026micro; denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive, R2\u0026thinsp;=\u0026thinsp;0.968, and ΔR2 obeys the Durbin-Watson distribution, which is a good fit and proves the existence of the moderator variable adherence.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Since compliance is a significant positive predictor of governance efficacy satisfaction, we will focus on demonstrating the impact of treatment process expectation and compliance on governance efficacy satisfaction and trying to show no significant interaction between the two, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e The graph illustrates the impact of treatment expectation and compliance on governance efficacy satisfaction. The solid line represents the relationship between treatment expectation and governance efficacy satisfaction, demonstrating how increases in treatment expectation contribute to higher levels of governance efficacy satisfaction.\u003c/p\u003e \u003cp\u003e The dashed line highlights the effect of compliance on governance efficacy satisfaction separately, indicating that higher compliance levels are associated with increased governance efficacy satisfaction. Adherence reflects the degree of patients' empathy with the treatment program, and adherence has a positive impact on treatment outcomes, indicating that the higher the empathy between patients and the treatment program, the higher the adherence and the higher the satisfaction with governance efficacy. The results can partially support hypothesis two.\u003c/p\u003e \u003cp\u003eA moderating effects analysis with treatment process expectations as the independent variable and governance efficacy satisfaction as the target variable was conducted to test Hypothesis III, while examining the moderating effects of physicians' online ratings and treatment costs and controlling for the following variables:\u003c/p\u003e \u003cp\u003e(1) total number of corresponding physician visits (2) attitudinal satisfaction (3) adherence (4) physician online ratings (5) access to treatment modalities\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of the moderating effect of Zscore (doctor online score) and Zscore (treatment cost \u003cspan\u003e$\u003c/span\u003e) on Zscore (treatment process expectations) on Zscore (satisfaction with governance effectiveness)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoeff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(total number of doctor visits)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(attitude satisfaction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Adherence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(access to treatment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Expectation of treatment process)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Cost of treatment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Doctor's online rating)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectation and Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectation and Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.984d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurbin Watson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the results of the analysis, it can be seen that the positive predictive effects of treatment process expectations and treatment cost on satisfaction with governance effectiveness were not significant (P\u0026thinsp;\u0026ge;\u0026thinsp;0.01),while the moderating variable, physician online ratings, had a significant positive predictive effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the interaction variable between the two was not significant at the P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 level, which suggests that there is no moderating effect of either physician online ratings or treatment costs in the relationship between treatment process expectations and satisfaction with governance effectiveness. There was no moderating effect between either satisfaction. Substituting (4) indicated a linear regression model as:\u003c/p\u003e \u003cp\u003e\u003cem\u003e(Satisfaction with governance efficacy)\u0026thinsp;=\u0026thinsp;0.019\u0026thinsp;+\u0026thinsp;0.026 * (Expectation of treatment process)\u0026thinsp;+\u0026thinsp;0.043 (Physician online rating)\u0026thinsp;+\u0026thinsp;0.019 (Treatment cost$)\u0026thinsp;+\u0026thinsp;0.021 (Interaction: expectation and rating)\u0026thinsp;+\u0026thinsp;0.022 (Interaction: expectation and cost)\u0026thinsp;+\u0026thinsp;0.023 (Corresponding to the total number of visits to the physician)\u0026thinsp;+\u0026thinsp;0.027 (Attitudinal satisfaction)\u0026thinsp;+\u0026thinsp;0.043* ( adherence)\u0026thinsp;+\u0026thinsp;0.019 (treatment modality access) + \u0026micro;\u0026thinsp;+\u0026thinsp;ε\u003c/em\u003e (7)\u003c/p\u003e\u003cp\u003ewhere \u0026micro; denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive with R2\u0026thinsp;=\u0026thinsp;0.969, which is a good fit and a robust model. It indicates that physician online ratings have a positive impact on treatment outcomes, and the higher the physician online rating, the higher the patient's satisfaction with governance effectiveness. The results can partially support hypothesis three.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;4\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis linear relationship will be simulated by constructing a hypothetical dataset and showing how the primary variables-treatment process expectations, physician online ratings, and treatment costs-alone and in combination affect governance efficacy satisfaction. In particular, since the effect of the interaction variables was shown to be not significant by the analysis, we focused on the direct effects of the primary variables rather than their interaction effects.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe graph illustrates the impacts of treatment expectation, doctor online rating, and treatment cost on treatment efficacy satisfaction. Each line represents a different relationship:\u003c/p\u003e \u003cp\u003eThe solid line shows how treatment efficacy satisfaction changes with varying levels of treatment expectation, indicating a direct relationship based on the model.\u003c/p\u003e \u003cp\u003eThe dashed line depicts the relationship between treatment efficacy satisfaction and doctor online rating, highlighting the significant positive effect that higher online ratings have on treatment satisfaction.\u003c/p\u003e \u003cp\u003eThe dot-dashed red line illustrates the effect of treatment cost on treatment efficacy satisfaction, suggesting a more complex relationship that might not be as straightforward as the other two factors.\u003c/p\u003e \u003cp\u003eThis visualization underscores the importance of both the patient's treatment expectation and the perceived quality of the doctor (as reflected in online ratings) in influencing treatment outcomes. It also acknowledges the role of treatment cost, although its direct impact on satisfaction might be nuanced and requires further investigation.\u003c/p\u003e \u003cp\u003eNotably, the analysis indicated that the interaction effects between these variables and treatment expectation were not significant, focusing instead on their direct influences.\u003c/p\u003e \u003cp\u003eIn order to test hypothesis four, we use the patient treatment process expectation on the telemedicine platform as the independent variable, and the doctor online score and evaluation score as the target variables, respectively, to study the moderating effects of the corresponding total number of doctor visits and the treatment cost between the treatment process expectation and the doctor online score, and between the treatment process expectation and the evaluation score. At the same time, considering other possible influences, we controlled for the following variables: (1) adherence (2) treatment modality access (3) attitude satisfaction (4) treatment efficacy satisfaction. The results of the analysis are shown in Tables VII and VIII:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of the moderating effect of Zscore (corresponding to the total number of doctor visits) and Zscore (treatment cost \u003cspan\u003e$\u003c/span\u003e) on Zscore (treatment process expectations) on Zscore (doctor online score)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoeff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Adherence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Treatment access)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Attitude satisfaction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Satisfaction with\u003c/p\u003e \u003cp\u003etreatment efficacy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Expectation of treatment process)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Total number of doctor visits)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Treatment cost\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectation and visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectation and Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.936d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurbin Watson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eBased on the results of the analysis in Table VII, it can be seen that none of the positive predictive effects of treatment process expectations, total number of visits to the corresponding physician, and cost of treatment on satisfaction with governance effectiveness were significant (P\u0026thinsp;\u0026ge;\u0026thinsp;0.01), and the interaction variable between the two was not significant at the P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 level, suggesting that there was no moderating effect of either total number of visits to the corresponding physician or cost of treatment between treatment process expectations and the physician's online rating. A linear regression model was expressed as:\u003c/p\u003e \u003cp\u003e\u003cem\u003e(Doctor Online Score)\u0026thinsp;=\u0026thinsp;0.037\u0026thinsp;+\u0026thinsp;0.053 * (Treatment Process Expectation)\u0026thinsp;+\u0026thinsp;0.047 (Corresponding to Total Number of Doctor Visits)\u0026thinsp;+\u0026thinsp;0.038 (Treatment Cost$)\u0026thinsp;+\u0026thinsp;0.033 (Interaction Factor: Expectation and Visits)\u0026thinsp;+\u0026thinsp;0.045 (Interaction Factor: Expectation and Cost)\u0026thinsp;+\u0026thinsp;0.171* (Adherence)\u0026thinsp;+\u0026thinsp;0.039 (Access to Treatment Modality)\u0026thinsp;+\u0026thinsp;0.054 ( Attitude satisfaction)\u0026thinsp;+\u0026thinsp;0.171 (Treatment efficacy satisfaction) + \u0026micro;\u0026thinsp;+\u0026thinsp;ε\u003c/em\u003e (8)\u003c/p\u003e \u003cp\u003ewhere \u0026micro; denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive with R2\u0026thinsp;=\u0026thinsp;0.875, which is a good fit.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of Moderating Effects of Zscore (Corresponding to Total Physician Visits) and Zscore (Treatment Cost \u003cspan\u003e$\u003c/span\u003e) on Zscore (Treatment Procedure Expectation) on Zscore (Evaluation Score)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoeff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Adherence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Treatment access)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Attitude satisfaction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Satisfaction with\u003c/p\u003e \u003cp\u003etreatment efficacy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Expectation of treatment process)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Total number of doctor visits)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Treatment cost)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectation and visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectation and Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e.882d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurbin Watson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e2.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eBased on the results of the analysis, it was found that the positive predictive effects of treatment process expectations, total number of visits to the corresponding physician, and treatment cost on the evaluation scores were not significant (P\u0026thinsp;\u0026ge;\u0026thinsp;0.01), while the interaction variables of the two were not significant at the P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 level, indicating that there were no moderating effects of total number of visits to the corresponding physician and treatment cost between treatment process expectations and evaluation scores. The linear regression model was:\u003c/p\u003e \u003cp\u003e\u003cem\u003e(Evaluation Score)\u0026thinsp;=\u0026thinsp;0.051\u0026thinsp;+\u0026thinsp;0.072 * (Treatment Procedure Expectations)\u0026thinsp;+\u0026thinsp;0.063 (Corresponding to Total Number of Physician Visits)\u0026thinsp;+\u0026thinsp;0.052 (Treatment Cost$)\u0026thinsp;+\u0026thinsp;0.044 (Interaction Factors: Expectations and Visits)\u0026thinsp;+\u0026thinsp;0.060 (Interaction Factors: Expectations and Costs)\u0026thinsp;+\u0026thinsp;0.238* (Adherence)\u0026thinsp;+\u0026thinsp;0.052 (Access to Treatment Modalities)\u0026thinsp;+\u0026thinsp;0.073 (Attitude satisfaction)\u0026thinsp;+\u0026thinsp;0.235 (treatment efficacy satisfaction) + \u0026micro;\u0026thinsp;+\u0026thinsp;ε\u003c/em\u003e (9)\u003c/p\u003e\u003cp\u003ewhere \u0026micro; denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive with R2\u0026thinsp;=\u0026thinsp;0.778, which is a fair fit. Therefore the results do not support hypothesis four.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThis linear relationship was simulated by constructing a hypothetical dataset and demonstrating how the primary variables \u0026mdash; treatment process expectations, corresponding physician total visits, treatment costs \u0026mdash; alone, and in combination, influenced the physician online score. In particular, emphasis was placed on the direct effect of the treatment process expectations and its potential interaction effects with the total number of visits to the corresponding physician and treatment costs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e The graph depicts the impact of treatment expectation on the doctor's online rating, illustrating a linear relationship based on the provided model.\u003c/p\u003e \u003cp\u003eThis visualization suggests that as treatment expectation increases, there's a corresponding increase in the doctor's online rating, reflecting the positive coefficient associated with treatment expectation in the model.\u003c/p\u003e \u003cp\u003eThis model controls for other factors such as the number of doctor visits and treatment cost, indicating that the primary focus is on the direct relationship between treatment expectation and online rating. The visualization simplifies the model by not explicitly showing the effects of control variables and potential interaction terms, focusing instead on the significant direct impact of treatment expectation. The visualization simplifies the model by not explicitly showing the effects of control variables and potential interaction terms, focusing instead on the significant direct impact of treatment expectation.\u003c/p\u003e \u003cp\u003eIn order to test hypothesis five, \"The Pygmalion effect will lead to higher and higher ratings of good doctors, and there is a reverse exacerbation effect,\" we constructed a model to study treatment efficacy satisfaction and doctors' online ratings as the independent and target variables, respectively, using linear regression while controlling for the following variables: (1) access to treatment modalities (2) Treatment cost (3) Attitude satisfaction (4) Treatment process expectations. The results are shown in Table IX and Table X:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression analysis of Zscore (treatment efficacy satisfaction) on Zscore (physician online score)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoeff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.71E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(access to healing modalities)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(treatment cost )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Attitude satisfaction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Expectation of treatment process)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(total number of doctor visits)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Satisfaction with treatment efficacy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e.934b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurbin Watson.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression analysis of Zscore (physician online score) on Zscore (satisfaction with treatment efficacy)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoeff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Variant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.58E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(access to healing modality)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(treatment cost )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(attitude satisfaction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Expectation of treatment process)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(total number of doctor visits)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Doctor's online rating)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e.933b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurbin Watson.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eBased on the results of the analysis, it was found that treatment efficacy satisfaction had a significant positive predictive effect (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on physician online ratings and physician online ratings on governance efficacy satisfaction, with linear regression models as:\u003c/p\u003e \u003cp\u003e \u003cem\u003e(doctor online score)\u0026thinsp;=\u0026thinsp;0.037\u0026thinsp;+\u0026thinsp;0.040 (treatment efficacy satisfaction)\u0026thinsp;+\u0026thinsp;0.037 (treatment modality access)\u0026thinsp;+\u0026thinsp;0.038 (treatment cost$)\u0026thinsp;+\u0026thinsp;0.054 (attitude satisfaction)\u0026thinsp;+\u0026thinsp;0.052 * (treatment process expectation)\u0026thinsp;+\u0026thinsp;0.046 (corresponding to the total number of doctor visits) + \u0026micro;\u0026thinsp;+\u0026thinsp;ε\u003c/em\u003e (10)\u003c/p\u003e \u003cp\u003e \u003cem\u003e(Satisfaction with treatment efficacy)\u0026thinsp;=\u0026thinsp;0.037\u0026thinsp;+\u0026thinsp;0.041 (doctor's online rating)\u0026thinsp;+\u0026thinsp;0.038 (access to treatment modalities)\u0026thinsp;+\u0026thinsp;0.038 (cost of treatment $)\u0026thinsp;+\u0026thinsp;0.054 (attitudinal satisfaction)\u0026thinsp;+\u0026thinsp;0.053 * (expectations of the treatment process)\u0026thinsp;+\u0026thinsp;0.046 (corresponding to the total number of doctor's visits) + \u0026micro;\u0026thinsp;+\u0026thinsp;ε\u003c/em\u003e (11)\u003c/p\u003e \u003cp\u003ewhere \u0026micro; denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term is positive with R2\u0026thinsp;=\u0026thinsp;0.871, which is a good fit, indicating the presence of moderating variables and high reliability of the model.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIn both models, we considered the controlling role of other variables such as treatment modality access route, treatment cost, attitude satisfaction, treatment process expectation and the total number of corresponding physician visits. The results of the model showed that there is a significant positive relationship between treatment efficacy satisfaction and doctors 'online score, which supports the existence of Pygmalion effect, that is, doctors' praise will lead to\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe two graphs illustrate the mutual influence between doctor online rating and treatment efficacy satisfaction, supporting the hypothesis that a positive loop exists\u0026mdash;higher treatment efficacy satisfaction leads to higher doctor online ratings and vice versa. The first graph (left) shows the effect of treatment efficacy satisfaction on doctor online rating. As treatment efficacy satisfaction increases, the doctor's online rating also increases, highlighting the direct positive relationship between these variables. The second graph (right) demonstrates the reverse relationship, where an increase in doctor online rating contributes to higher treatment efficacy satisfaction. This again confirms the positive feedback loop between these variables, aligning with the Pygmalion effect hypothesis. These visualizations encapsulate the essence of Hypothesis Five, demonstrating the interconnectedness and reinforcing nature of treatment satisfaction and physician ratings within the context of remote medical platforms. Therefore, hypothesis five is supported.\u003c/p\u003e \u003cp\u003eIn order to test hypothesis six, we investigated the moderating effects of adherence and attitudinal satisfaction between treatment process expectations and treatment efficacy satisfaction on the telemedicine platform with patient treatment process expectations as the independent variable and treatment efficacy satisfaction as the target variable, respectively. Meanwhile, considering other possible influencing factors, we controlled for the following variables: (1) treatment modality access (2) doctor online rating (3) total number of visits to the corresponding doctor (4) treatment cost. The results of the analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of moderating effects of Zscore (adherence) and Zscore (attitude satisfaction) on Zscore (treatment process expectations) on Zscore (treatment efficacy satisfaction).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoeff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Access to treatment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Doctor's online score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Total visits to corresponding doctors)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Treatment cost)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Expectation\u003c/p\u003e \u003cp\u003eof treatment process)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Compliance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZscore(Attitude satisfaction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectation and Attitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectation\u003c/p\u003e \u003cp\u003eand adherence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e.985d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurbin-Watson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the results of the analysis, it can be seen that the positive predictive effect of treatment process expectations and attitude satisfaction on satisfaction with governance efficacy is not significant (P\u0026thinsp;\u0026ge;\u0026thinsp;0.01), while the moderator variable of adherence has a significant positive predictive effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the interaction variable of the two is not significant at the level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, which indicates that there is no moderating effect of adherence and attitude satisfaction between treatment process expectations and governance efficacy satisfaction Neither had a moderating effect. Substituting (4) indicates a linear regression model as:\u003c/p\u003e\u003cp\u003e\u003cem\u003e(Satisfaction with treatment efficacy)\u0026thinsp;=\u0026thinsp;0.019\u0026thinsp;+\u0026thinsp;0.026* (treatment process expectations)\u0026thinsp;+\u0026thinsp;0.043 (adherence)\u0026thinsp;+\u0026thinsp;0.028 (attitudinal satisfaction)\u0026thinsp;+\u0026thinsp;0.025 (interaction: expectations and attitudes)\u0026thinsp;+\u0026thinsp;0.024 (interaction: expectations and adherence)\u0026thinsp;+\u0026thinsp;0.019 (access to treatment modalities)\u0026thinsp;+\u0026thinsp;0.023 (corresponding to the total number of physician visits)\u0026thinsp;+\u0026thinsp;0.019 ( cost of treatment $)\u0026thinsp;+\u0026thinsp;0.042 (physician online rating) + \u0026micro;\u0026thinsp;+\u0026thinsp;ε\u003c/em\u003e (12)\u003c/p\u003e\u003cp\u003ewhere \u0026micro; denotes the capture of unobserved individual-specific effects and ε it denotes the residual random error term. The coefficient of the interaction term was positive with R2\u0026thinsp;=\u0026thinsp;0.969, which is a good fit and a reliable model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe graph showcases the relationship between treatment expectation and treatment efficacy satisfaction, with adjustments for compliance and attitude satisfaction.\u003c/p\u003e \u003cp\u003eThis visualization indicates that as treatment expectation increases, there is a corresponding increase in treatment efficacy satisfaction, factoring in the positive influences of compliance and attitude satisfaction. This model highlights the significant role of patient compliance and their satisfaction with the treatment's approach, supporting the hypothesis that higher compliance and a positive attitude towards the treatment process contribute positively to the overall treatment efficacy satisfaction. It demonstrates the interconnectedness of these factors in the remote healthcare context, where patient expectations, compliance, and attitudes are crucial determinants of treatment outcomes.\u003c/p\u003e \u003cp\u003eIt indicates that patient adherence has a positive effect on treatment outcomes, and the greater the patient adherence, the higher the satisfaction with treatment efficacy. The results can partially support part of hypothesis six, which \u003cem\u003ecan\u003c/em\u003e be described as \"the follow-up process of the doctor's expectations of the patient, the patient's self-efficacy, adherence and the patient's recovery effect have a positive relationship\".\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e6.1. key findings\u003c/h2\u003e \u003cp\u003eOur study explored multiple factors influencing patients' satisfaction with physicians' treatment efficacy on a telemedicine platform by constructing a model based on multiple linear regression and moderated regression. By analyzing the data, we found that the variables of access to treatment, doctor online rating, total visits to the corresponding doctor, treatment cost, expectations of the treatment process, adherence, and attitudinal satisfaction had a significant effect on the satisfaction with treatment efficacy. Among these variables, physician online rating and adherence were significant positive predictors of satisfaction with treatment efficacy, whereas the relationship between treatment expectations and satisfaction with treatment efficacy was moderated by adherence. In addition, we found no moderating effect of patient adherence between treatment process expectations and treatment efficacy satisfaction. Through robustness checks, we further confirmed the reliability and stability of our findings. These findings contribute to a deeper understanding of the formation mechanism of patients' satisfaction with doctors' treatment efficacy on telemedicine platforms, and provide useful insights and suggestions for improving the quality of telemedicine services.\u003c/p\u003e \u003cp\u003eFrom the results of models (1) and (2), it can be seen that treatment process expectations have a significant positive effect on both treatment satisfaction and evaluation scores. This suggests that the higher the patients' expectations of the treatment process, the higher their treatment satisfaction and evaluation scores will be accordingly. This result is consistent with our hypothesis, suggesting that patients' expectations of the treatment process have a significant effect on their efficacy satisfaction and evaluation, and that patients are more inclined to choose doctors and treatment programs on telemedicine platforms that meet their original expectations, and that this reduces the efficacy of their treatment when they suffer from serious illnesses. Satisfaction with and evaluation of treatment can indirectly reflect the effectiveness of treatment, thus proving Hypothesis 2: Patient's expectations will greatly influence the effectiveness of treatment.\u003c/p\u003e \u003cp\u003eHowever, we also need to be aware of the influence of other factors. For example, doctors' online ratings demonstrated a significant positive association between treatment effectiveness satisfaction and evaluation scores. This implies that physician professionalism and the quality of online services are also important factors in patient satisfaction and evaluation. Similarly, access to treatment modalities had a significant positive effect on evaluation scores, suggesting that patients' ease of access to treatment modalities affects their evaluations of treatment, thus validating our hypothesis that \"patients are more likely to receive medical information that is consistent with their existing beliefs or expectations, and that this information affects the strength of the Pygmalion effect\". The coefficients of treatment cost and physician online rating in the two models are positive, in which treatment cost has a significant effect on evaluation scores, and physician online rating has a significant effect on satisfaction with treatment efficacy, indicating that treatment cost has a positive effect on evaluation scores, and physician online rating has a positive effect on satisfaction with treatment efficacy to varying degrees. The treatment cost can reflect the doctor's treatment expectation, and the increase of cost can affect the doctor's treatment cost and motivation, and the doctor's online score also has a positive incentive effect on the doctor's treatment expectation, so it can indirectly reflect the reasonableness of hypothesis three, \"The doctor's expectation of the patient plays a positive role in the creation of the principle of consistency of the multiple aspects\". Meanwhile, the positive and significant effect of physicians' online ratings on satisfaction with treatment efficacy also reflects that the higher the physicians' online ratings, the higher the patients' satisfaction with treatment efficacy, thus proving that physician ratings have an exacerbating effect on patients' satisfaction and evaluation in hypothesis five. Cost and online scores led to an increase in physician expectations of patients, and the positive effect of physician expectations on recovery outcomes in hypothesis six was also verified. Adherence has a significant positive effect on evaluation scores, indicating that patients' own behavioral adherence will improve their evaluation of treatment, but the effect of adherence on satisfaction with treatment efficacy and attitudinal satisfaction is not statistically significant, while the recovery effect is influenced by a large number of factors, and Hypothesis six, \"Subsequent adherence in the process of physician's follow-up with the patient will have a two-fold effect on the recovery effect \"To be further investigated. Hypothesis four, \"Expectations from platforms, patients, and physicians themselves have an impact on physicians' career prospects,\" needs to be verified while controlling for other variables to determine the effects of expectations of the treatment process, physicians' online ratings, and attitudinal satisfaction on the target variables corresponding to the total number of physician visits and physicians' online ratings; the first half of hypothesis five, \"Pygmalion\", is a hypothesis. \"The Pygmalion effect will result in higher and higher ratings for good doctors\" needs to be verified for the significance of treatment efficacy satisfaction on the target variable doctor online scores; and the second half of hypothesis six after the doctor's expectations of the patient, patient self-efficacy, and adherence will have positive and negative effects on the recovery effect also needs to be continued to analyze the effects of patient adherence, attitudinal satisfaction, and doctor's expectations of the patient on the treatment effectiveness of the reverse effect of satisfaction, if necessary, can increase the variables to increase the representativeness, in order to fully reflect the situation of the patient's recovery effect, while the patient's self-efficacy has not yet been confirmed to have a significant impact on the recovery effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Limitations and future directions\u003c/h2\u003e \u003cp\u003eTo further explore the effect of treatment process expectations on evaluation scores, we conducted moderated effects analyses for each of the six hypotheses. The results of these analyses indicated that: treatment process expectations had a positive predictive effect on the total number of visits to the corresponding physician, while treatment modality access had a lesser effect on the total number of visits to the physician, and Hypothesis I was supported; the stronger the patient's empathy with the treatment plan, the higher the adherence, and the higher the satisfaction with governance efficacy, and thus the results could partially support Hypothesis II; the physician's online score had a positive impact on the treatment efficacy, and the results could partially support Hypothesis three; the results do not support hypothesis four; treatment effectiveness satisfaction has a significant positive predictive effect on physician online ratings and physician online ratings have a positive predictive effect on governance effectiveness satisfaction, so hypothesis five is supported; patient adherence has a positive effect on treatment effectiveness, and the results can partially support hypothesis six. This can be described as \"physician expectations, patient self-efficacy, and adherence are positively correlated with recovery outcomes during subsequent physician follow-up with the patient.\"\u003c/p\u003e \u003cp\u003eExpectation of the treatment process was an independent influence on the evaluation scores, and its effect was statistically significant. To ensure the stability and reliability of our findings, we also conducted an R2 fit comparison, and we found that all hypotheses except Hypothesis 1 had a high fit, indicating the robustness of Models 6\u0026ndash;11. This further supports our hypotheses and conclusions about the effect of treatment process expectations on evaluation scores. However, the study of the Pygmalion effect on physicians' career prospects we have left at the theoretical level, i.e., through sentiment analysis, it was concluded that physicians with higher titles and career levels are more likely to influence career choices when they receive positive feedback originating from their patients and surroundings. For the study of physician-patient interactions during treatment follow-up, we only concluded a positive influence.\u003c/p\u003e \u003cp\u003eOverall, our findings suggest that treatment process expectations are an important factor influencing patient satisfaction and evaluation scores. On telemedicine platforms, healthcare organizations should pay attention to patients' treatment process expectations and take steps to meet or exceed them in order to improve patient satisfaction and evaluation scores. At the same time, healthcare organizations also need to focus on the professionalism of physicians and the quality of online services, as well as other influencing factors such as the convenience of treatment modalities and patient compliance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Practical implications:\u003c/h2\u003e \u003cp\u003eFirst, we found that patients are more inclined to believe information from sources that match their beliefs and expectations, and that patients' expectations and beliefs will greatly influence medical outcomes. Therefore, patients should place themselves in a rational perspective when choosing medical information, rather than believing that information that matches their expectations and beliefs is helpful.\u003c/p\u003e \u003cp\u003ePatients who are confident in the treatment process are more likely to experience faster recovery and to be satisfied with the overall healthcare experience, and this result provides physicians with new ideas for the treatment process. Proposing a treatment plan that is consistent with the patient's beliefs from the patient's point of view can be more acceptable to the patient, and raising the patient's expectations and understanding the patient's personalized needs through verbal and behavioral means can significantly Secondly, the doctor's expectations of the patient are more acceptable to the patient.\u003c/p\u003e \u003cp\u003eSecond, physician expectations of patients can lead to the creation of multifaceted congruence. When a physician confers an expectation on a patient, the patient is more likely to make consistent choices, choosing to accept consistent information, and will then realize that this coincides with the theory of multifaceted congruence in the Pygmalion effect Similarly, physicians are able to optimize healthcare teamwork, communication and shared expectations between physicians and other healthcare professionals by intentionally guiding the patient in the direction of established treatments in the course of their communication with the patient, which An environment more conducive to the patient's recovery can be created. This will lead to a positive effect on the patient's own process of internalizing the information. Finally, by analyzing the sentiment of user ratings on healthcare platforms, we found that the Pygmalion effect will lead to higher and higher ratings for good doctors, which in turn reverses the effect. It also gives each of us a new way of thinking when choosing a doctor; focusing on doctors with high ratings may not make our situation better, but instead focusing on healthcare professionals who match and are more able to help us with our condition.\u003c/p\u003e \u003cp\u003eDuring follow-up, physicians should perpetuate positive expectations and improve patient self-efficacy, and this study can be used to improve the training and education of healthcare professionals. Developing awareness of positive expectations for patients and effective communication skills among physicians and other healthcare practitioners can help improve the quality of healthcare services. It is also important for patients to maintain as high a level of self-efficacy and adherence as possible during the follow-up phase after treatment is completed, and the interplay between the two will maximize the effectiveness of the follow-up process.\u003c/p\u003e \u003c/div\u003e"},{"header":"7 Conclusion","content":"\u003cp\u003ePotential healthcare customers can find lower search expenses by participating in online health communities. In addition, patients and doctors find the online healthcare services market appealing due to the convenience of advocacy, counseling, and treatment as well as the platforms' profitability. On the other hand, not much study has been done on how effective internet healthcare can be for patients in terms of therapy. It would be beneficial to research the patient treatment procedure. It will shed light on how the patient treatment procedure affects the Pygmalion effect. We find that patients' information choices, treatment expectations, self-efficacy, and physicians' expectations may influence patient treatment as well as follow-up using a longitudinal dataset from three major healthcare websites and machine learning techniques. Simultaneously, this may influence modifications in the ratings of physicians. Specifically,\u003c/p\u003e \u003cp\u003ePatients with more complicated illnesses are more susceptible to this impact, which should be avoided. Our study explains the importance of physician traits and website features, and it offers important insights into how patients choose doctors and how doctors treat patients. Related studies on functionality, service surveys, and utilization in the online healthcare industry may be able to draw attention to and support the Pygmalion effect.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCRediT authorship contribution statement\u003c/p\u003e\n\u003cp\u003eXin Shen: Conceptualization, Methodology, Formal analysis, Writing \u0026ndash;review \u0026amp; editing, Writing \u0026ndash; original draft, Data curation. Yulin Yan: Conceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing, Software, Data curation, Investigation. Huikang Liu: Validation, Resources. Conceptualization, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial\u003c/p\u003e\n\u003cp\u003einterests or personal relationships that could have appeared to influence\u003c/p\u003e\n\u003cp\u003ethe work reported in this paper.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eA portion of the data that support the findings of this study are available from National Population Health Data Center at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncmi.cn/\u003c/span\u003e\u003c/span\u003e and The other part comes from\u003c/p\u003e\n\u003cp\u003eLarge online health sites.(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.webmd.com/,https://www.haodf.com/\u003c/span\u003e\u003c/span\u003e ,\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u0026nbsp;\u003cspan class=\"RefSource\"\u003ehttp://www.amazonaws.cn,https://www.practo.com/\u003c/span\u003e \u0026nbsp;\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eEthical and informed consent for data used This article does not contain any studies with human participants or animals performed by the author. We obtain ethical and informed consent from data subjects before collecting, using, or disclosing their personal data.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eXin Shen: Conceptualization, Methodology, Formal analysis, Writing \u0026ndash;review \u0026amp; editing, Writing \u0026ndash; original draft, Data curation. Yulin Yan: Conceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing, Software, Data curation, Investigation. Huikang Liu: Validation, Resources. Conceptualization, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAntonio S, Joseph D, Parsons J, Atherton H (2024) Experiences of remote consultation in UK primary care for patients with mental health conditions: A systematic review. 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Electron Commer Res Appl 52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.elerap.2022.101125\u003c/span\u003e\u003cspan address=\"10.1016/j.elerap.2022.101125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pygmalion effect, interaction, machine learning, online health community, multiple linear regression (MLR), emotional analysis","lastPublishedDoi":"10.21203/rs.3.rs-4449255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4449255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn addition to exploring how people's expectations and beliefs about healthcare information and services affect their actual health outcomes, this study aims to empirically analyze whether there is a Pygmalion effect in healthcare platforms using machine learning and natural language processing. Regression modeling will be used to provide treatment recommendations for more common diseases. We gathered a 10-month panel dataset from a major Chinese online medical portal, containing information from 10,243 physicians. We discovered a strong linear correlation between users' expectations for their final level of recovery and satisfaction and their access to doctors, medical information, treatment alternatives, and healthcare experiences. People's choice of therapy for more complicated illnesses, like heart valve lesions and breast cancer, should lean more away from conventional information sources. Patients' expectations and treatment adherence are strongly connected with the expectations of their doctors, and treatment outcomes are also significantly influenced by the beliefs and expectations of the patients themselves. Using sentiment analysis and multiple robustness polls of user ratings on healthcare platforms, we demonstrate that the treatment choices made by users are distributed linearly across various complexity levels of diseases. As a result, this study highlights the real influence of patient and physician expectations and beliefs on healthcare outcomes, proves the presence of the Pygmalion effect on healthcare platforms, and explores it for particular diseases.\u003c/p\u003e \u003cp\u003eThis has real-world implications for raising patient happiness, enhancing medical service quality, and strengthening the doctor-patient bond.\u003c/p\u003e","manuscriptTitle":"Do people only believe what they want to believe? An empirical analysis of the Pygmalion effect in telemedicine platforms based on linear regression algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 15:41:20","doi":"10.21203/rs.3.rs-4449255/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-05-27T15:14:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-21T02:30:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Machine Learning and Cybernetics","date":"2024-05-20T12:59:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"22178f16-b272-4d2b-b911-8b67df915bf5","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-06T15:41:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-06 15:41:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4449255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4449255","identity":"rs-4449255","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}