A Study on the Participation of Lung Cancer Patients in Surgical Decision-Making and Its Influencing Factors from the Perspective of Ecological Systems Theory

A Study on the Participation of Lung Cancer Patients in Surgical Decision-Making and Its Influencing Factors from the Perspective of Ecological Systems Theory | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Study on the Participation of Lung Cancer Patients in Surgical Decision-Making and Its Influencing Factors from the Perspective of Ecological Systems Theory Jiajie Pu, Yanxia Hu, Changying Zhang, Zhengchuan Dong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7019179/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Aim. To investigate the surgical decision-making and its influencing factors in patients with stages I to IIIa non-small cell lung cancer (NSCLC) under Ecological Systems Theory. Methods. Convenience sampling was used to select 219 surgical patients with stages I to IIIa NSCLC admitted to a tertiary hospital from February 2024 to June 2024, and an electronic questionnaire survey was distributed. The questionnaire included a general information survey, a Control Preference Scale (CPS), and a survey on patients' level of knowledge about medical decisions. Spearman's rank correlation analysis and multiple logistic regression analysis were used to explore the types of surgical decision-making participation and their influencing factors in patients with stages I to IIIa NSCLC. Results. Among the types of participation in surgical treatment decision-making for lung cancer patients, passive decision-making was the most common. Correlation analysis revealed that the type of participation in surgical treatment decision-making was negatively correlated with age and having commercial insurance (both P < 0.01), and positively correlated with educational level, chosen surgical method, and medical decision-making awareness (all P < 0.05). Multiple logistic regression analysis found that age, level of medical decision-making knowledge, and the presence of commercial insurance were influencing factors for the type of surgical decision-making participation (all P < 0.05). Conclusion . Patients who are younger, more informed about medical decisions, and hold commercial insurance are more inclined to adopt shared decision-making or proactive decision-making. Based on the Ecological Systems Theory, at the micro level, healthcare professionals should actively provide targeted decision-making support to promote patients' deeper involvement in surgical decision-making, thereby improving decision quality and health outcomes. At the macro level, medical institutions can strengthen cooperation with insurance companies to design and popularize commercial insurance products that meet patients' needs. Lung cancer Decision-making participation Decision-making expectation Influencing factors 1. Introduction According to the 2022 epidemiological analysis of malignant tumors in China, lung cancer still ranks first in both incidence and mortality among major tumors( 1 ). From the perspective of pathology and treatment, lung cancer can be mainly divided into two major categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), the proportion of non-small cell carcinoma is relatively higher, accounting for more 80%~85%༈2༉. The treatment methods for lung cancer include surgical resection, chemotherapy, targeted therapy, neoadjuvant immunotherapy, radiotherapy, and palliative treatment, etc༈3༉. Among the commonly used treatment methods, anatomic pulmonary surgery resection remains an important clinical method for early to mid-stage lung cancer༈2༉, and the 5-year survival rate of early-stage NSCLC patients after surgical resection is generally between 70% and 80%༈4༉. Among the various medical methods, timing and correct method are crucial for the prognosis and healing progression of patients. Therefore, the importance of clinical medical decision-making is becoming increasingly prominent. It refers to a process of determining who should choose which medical intervention and when the intervention begins to select the most appropriate medical intervention measures( 5 – 6 ). Looking back at the history of medical development in Western countries, with the deepening understanding of doctors' authority and patients' rights, the medical decision-making model has undergone three major renovations: from the paternalistic decision-making model to the informed decision-making model, and further into the shared decision-making model༈7༉. Although the shared decision-making model has been widely applied in the medical field, such as in chronic disease care and cancer treatment( 8 ), however, influenced by the Confucian tradition, there is a significant difference between China's medical decision-making model and that of the West. Specifically, doctors usually first inform the patient's family of the diagnosis, and then the family members discuss together whether to inform the patient and how to choose the appropriate treatment plan༈9༉. Li Yu༈10༉ and others mentioned in the decision-making of liver cancer surgery that patients are in a passive position in informed consent and their actual participation in surgical decision-making is less than their expectations. Yan Caidie༈11༉ and others pointed out that most of Chinese gynecological tumor patients are passive in surgical decision-making. There are relatively few studies on the surgical decision-making of patients with stages I to IIIa NSCLC, and this patient group's surgical treatment decision-making participation, information access channels, and decision satisfaction are worth exploring. When analyzing the surgical decision-making behavior of lung cancer patients, we can understand the underlying driving factors from a broader perspective. Based on the Ecological Systems Theory, an individual's behavior and decision-making do not exist in isolation but are influenced by micro, meso, and macro systems.Therefore,this study aims to adopt the Ecological Systems Theory as the perspective and use a cross-sectional survey to comprehensively analyze the current status of surgical treatment decision-making participation among patients with Stage I to IIIA non-small cell lung cancer (NSCLC). The study will take the patients' microsystem (educational level, presence of comorbid conditions, degree of financial burden related to medical care, and extent of awareness of medical decision-making), mesosystem (household registration type, employment status, and per capita family income level), and macrosystem (type of basic medical insurance and commercial insurance status) as the hypothetical independent variables, and explore the associations between these variables and surgical treatment decisions. This will lay a scientific foundation for optimizing the decision-making process and improving patient satisfaction, and also provide targeted guidance for healthcare professionals to encourage active patient participation in decision-making, ultimately enhancing decision quality and treatment outcomes. 2. Methods 2.1 Research Objects From February 2024 to June 2024, convenience sampling was used to select 219 postoperative lung cancer patients in the Department of Thoracic Surgery of a tertiary general hospital as research objects. Inclusion criteria: aged ≥ 18 years; diagnosed with stage I to IIIa lung malignant tumors according to NCCN guidelines( 12 ); the patient has consented about the condition; has undergone surgical treatment; consented and voluntarily participated in this study. Exclusion criteria: individuals with metastatic tumors; patients with mental illness, cognitive dysfunction, communication disorders, and other severe physical illnesses. This study has been approved by the Ethics Committee of Ruijin Hospital Shanghai Jiao Tong University School of Medicine.This research strictly followed the principles stated in the Declaration of Helsinki. 2.2 Research Methods 2.2.1 Sample Size Calculation Since the surgical decision-making type investigated in this study is a categorical variable, so a single proportion estimation method was considered for sample size calculation. Referring to the maximum proportion of 43.4% in three categories from previous literature( 13 ), α=0.05, allowable error δ is 5%, and the required sample size was calculated to be 213 cases using PASS software. Considering a non-response rate of 10%, a planned sample of 234 cases was included. A total of 234 questionnaires were distributed, and 219 valid questionnaires were collected, with a valid recovery rate of 93.6%. 2.2.2 Research Tools ( 1 ) General information survey, including gender, education level, marital status, household registration type, employment status, basic medical insurance type, whether holding commercial insurance, whether having underlying diseases, degree of medical economic burden, and family per capita income level. ( 2 ) Control Preference Scale(CPS) was originally developed by Canadian scholars Degner et al.( 14 ) and translated and processed by Xu Xiaolin et al.༈15༉. In this study, we used this scale to assess the actual participation type of lung cancer patients in the surgical decision-making process. The scale consists of 5 options from A to E, where A means the doctor made the decision entirely; B means the doctor made the decision after seriously considering patient’s thoughts; C means the patient and the doctor made the decision together after comprehensive consideration; D means the patient made the decision after seriously considering the doctor's advice; E means the patient made the decision after understanding all medical choices. Choosing A or B is considered passive decision-making; choosing C is considered shared decision-making; choosing D or E is considered proactive decision-making. This decision scale is simple and clear, easy to fill out, time-saving, and can quickly and effectively assess the patient's surgical decision-making tendency. The scale language is easy to understand, facilitating patient comprehension and improving data collection efficiency. The scale's Cronbach's α coefficient is 0.899, and due to its high reliability, it is often used in domestic and foreign related research༈15,16–18༉. ( 3 ) Patient's Knowledge of Medical Decisions Survey Form, developed by Xu Xiaolin( 15 ), is used to measure the lung cancer patients' knowledge of surgical decisions, with the following 5 questions: ① The benefits and risks of choosing treatment versus non-treatment; ② What treatment options are available; ③ The benefits of the chosen treatment plan and the expected results; ④ The potential risks of the chosen treatment plan; ⑤ The expected situation during the implementation of the chosen treatment plan. This study uses the Likert 5-point scoring method for quantification, with specific levels being: "Very Unfamiliar" (1 point), "Not Very Familiar" (2 points), "Moderately Familiar" (3 points), "Fairly Familiar" (4 points), and "Very Familiar" (5 points). The total score range is set from 5 to 25 points, with higher scores indicating a higher level of knowledge. Additionally, the Cronbach's α coefficient for this study is 0.806, showing good reliability. 2.3 Data Collection Method Data was collected within 5 days after the patient's surgery using an offline anonymous questionnaire. Before the distribution of each questionnaire, the researcher or interviewer explained the purpose of the study to the patients in detail and obtained their informed consent before distributing the questionnaire. During the filling process, the researcher or interviewer used united guidance and answered any questions the patients had. After the patients completed the questionnaire, it was collected on the spot. 2.4 Statistical Processing SPSS 25.0 statistical software was used. Quantitative data is represented by x ± s or M(Q1, Q3); count data is represented by the number of cases and percentage. The correlation between ordinal variables or rank variables was analyzed using Spearman's rank correlation analysis. In single-factor analysis and correlation analysis, variables with statistical significance were selected, and a multifactor logistic regression analysis model was established based on these variables. A P-value of less than 0.05 was considered statistically significant. 3. Results 3.1 General Information of Research Subjects A total of 219 patients with stages I to IIIa NSCLC, with an average age of 52.68 ± 13.64 years, and other data is shown in Table 1 . Table 1 General Information of Research Subjects (n = 219) Item Classification Number of Cases (n) Percentage (%) Gender Male 111 50.7% Female 108 49.3% Education Level Primary School and below 18 8.2% Junior High School 75 34.2% High School/Technical School/Vocational School 55 25.1% College 23 10.5% Bachelor's Degree 40 18.3% Master's Degree and above 8 3.7% Marital Status Married 198 90.4% Single 16 7.3% Divorced and Widowed 5 2.3% Household Registration Type Urban 163 74.4% Rural 56 25.6% Employment Status Employed 107 48.9% Unemployed and Not Retired 37 16.9% Retired 75 34.3% Basic Medical Insurance Type Shanghai Employee Medical Insurance 45 20.5% Shanghai Resident Medical Insurance 17 7.8% Inter-regional Medical Insurance 153 69.9% No Social Insurance 4 1.8% Holding Commercial Insurance Yes 58 26.5% No 161 73.5% Underlying Diseases Yes 53 24.2% No 166 75.8% Medical Economic Burden Very Heavy 15 6.8% Heavier 36 16.4% General 145 66.2% Lighter 18 8.2% Family Per Capita Income Level 3000 and below 32 14.6% 3000–5000 90 41.1% 5000–10000 56 25.6% 3.2 Types of Surgical Treatment Decision-Making Participation by Lung Cancer Patients Among the types of participation in surgical treatment decision-making for lung cancer patients, passive decision-making accounted for 86 cases (39.3%), shared decision-making for 81 cases (37.0%), and proactive decision-making for 52 cases (23.7%). For more details, see Table 2 . Table 2 Types of Surgical Treatment Decision-Making Methods for Lung Cancer Patients (n = 219) Item Classification Number of Cases (n) Percentage (%) Passive Decision-Making Option A 26 11.9 Option B 60 27.4 Shared Decision-Making Option C 81 37.0 Proactive Decision-Making Option D 49 22.4 Option E 3 1.4 3.3 Correlation Analysis of Types of Surgical Treatment Decision-Making Participation by Lung Cancer Patients The results of Spearman's rank correlation analysis indicate that there is a significant negative correlation between the participation types of lung cancer patients in the surgical treatment decision-making process and their age (P < 0.001) and whether they have commercial insurance (P < 0.001), meanwhile there is a positive correlation with educational level (P = 0.001), chosen surgical method (P = 0.017), and awareness of medical decision-making (P < 0.001), see Table 3 . Table 3 Correlation Analysis of Types of Surgical Treatment Decision-Making Participation by Lung Cancer Patients (n = 219) Item r Value P Value Age -0.396** < 0.001 Gender 0.078 0.248 Education Level 0.214** 0.001 Marital Status 0.04 0.557 Household Registration Type -0.071 0.292 Employment Status -0.075 0.266 Basic Medical Insurance Reimbursement Type -0.008 0.908 Commercial Insurance -0.227** < 0.001 Underlying Diseases (Hypertension, Diabetes, etc.) 0.091 0.18 Medical Economic Burden 0.006 0.93 Family Per Capita Monthly Income Level 0.097 0.152 First Hospitalization -0.074 0.275 First Lung Surgery 0.031 0.651 Overall Assessment of Own Health Status 0.065 0.34 Chosen Surgical Method 0.162* 0.017 Awareness of Medical Decision-Making 0.563** < 0.001 *Note: * P < 0.05, ** P < 0.01 3.4 Multiple Logistic Regression Analysis of Types of Surgical Treatment Decision-Making Participation by Lung Cancer Patients Taking the participation type in lung cancer patients' surgical treatment decision-making as the dependent variable (passive decision-making = 1, shared decision-making = 2, proactive decision-making = 3; using passive decision-making as control), and using the items with statistical significance in Table 3 as independent variables (continuous variables are entered as original values; dummy variables are assigned values. Commercial insurance: 1 = Yes, 2 = No), multiple logistic regression analysis was performed with α entry = 0.05 and α exit = 0.10. The results show that the variables entering the regression equation are age, awareness of medical decision-making, and whether there is commercial insurance, see Table 4 . Table 4 Multiple Logistic Regression Analysis of Types of Surgical Treatment Decision-Making Participation by Lung Cancer Patients (n = 219) Dependent Variable varible B SE Waldχ༒ P OR 95% Confidence Interval Upper limit Lower limit Proactive Decision-Making Age -0.07** 0.021 10.818 0.001 0.932 0.894 0.972 Awareness of Medical Decision-Making 0.634** 0.102 38.946 < 0.001 1.884 1.544 2.299 Commercial Insurance 1.325* 0.575 5.315 0.021 3.761 1.22 11.599 Shared Decision-Making Age -0.044* 0.014 9.458 0.002 0.957 0.93 0.984 Awareness of Medical Decision-Making 0.138** 0.052 7.038 0.008 1.147 1.037 1.27 Commercial Insurance 0.841* 0.417 4.059 0.044 2.319 1.023 5.254 *Note: * P < 0.05, ** P < 0.01 4. Discussion 4.1 Current Status of Lung Cancer Patients' Participation in Surgical Treatment Decision-Making This study explored the current status of lung cancer patients' participation in surgical treatment decision-making and conducted a multivariate logistic regression analysis of its influencing factors. The results show that among lung cancer patients, the proportions of passive decision-making, shared decision-making, and proactive decision-making are 39.3%, 37.0%, and 23.7%, respectively. This result differs significantly from Zhang Jina's( 19 ) study on non-small cell lung cancer patients' surgical decision-making types, which reported 67.3% passive decision-making. It is evident that although a considerable number of patients play a relatively passive role in the treatment process, the increased proportions of shared decision-making (37.0%) and proactive decision-making (23.7%) indicate that more patients are beginning to take the initiative in treatment decision-making. Possible reasons include: ① The two studies were conducted in different regions, and the patient groups are in different social environments and cultural backgrounds; ② With the popularization of medical knowledge and the improvement of patients' education levels, patients pay more attention to their health status, have more understanding and knowledge of diseases and treatment plans, and are more willing to participate in the decision-making process; ③ The diversification of patients' medical information acquisition channels enables patients to participate more actively in treatment decision-making; ④ There is a 6-year gap between the two studies, and in recent years, medical system reforms, such as promoting patient-centered medical service models and encouraging patient participation in decision-making, have increased patients’ involvement; ⑤ Compared to previous studies, this study has a larger sample size and may more accurately reflect the current actual situation of lung cancer patients' participation in surgical treatment decision-making. The degree of passive decision-making among thoracic surgery patients (39.3%) is close to that of general surgery patients (43.40%)( 20 ) and neurosurgery stroke surgery patients (44.44%)༈21༉ in previous domestic studies but significantly different from foreign studies using the Decision Expectancy Scale (chronic kidney disease patients: 8%༈22༉; hand surgery patients: 22%༈23༉). This may be due to various factors such as culture, education level, health literacy, and medical systems between China and other countries, and it also indicates that the initiative and enthusiasm of Chinese patients in treatment decision-making need to be strengthened. 4.2 Factors Influencing Surgical Treatment Decision-Making Participation in Lung Cancer Patients from the Perspective of Ecological Systems Theory Based on the Ecological Systems Theory, individual behaviors and decisions are influenced by the micro, meso, and macro systems. This study analyzes the factors influencing surgical treatment decision-making participation among lung cancer patients within the framework of Ecological Systems Theory. 4.2.1 Microsystem: The Impact of Individual Characteristics on Decision-Making Participation Among the variables in the microsystem, this study found that age has a negative impact on the surgical treatment decision-making participation of lung cancer patients. That is, the older the patient, the more passive their decision-making participation. This is similar to the findings of a study on medical decision-making types among adults and another on medical decision-making types among prostate cancer patients ( 24 ). The increase in age may imply a decline in the ability to obtain medical information, as well as the capacity to understand and accept new things and technologies ༈25༉. Against the backdrop of building age-friendly hospitals, it is essential to pay more attention to the needs and difficulties of elderly patients in medical decision-making, and to provide more humanized services and supportive tools to help them better understand treatment options and related risks, thereby enhancing their participation in decision-making. In medical practice, doctors and nurses should provide more support and guidance to elderly patients, offering more easy-to-understand educational videos and communication strategies to improve their decision-making participation. Another key variable in the microsystem is the degree of awareness of medical decision-making, which has a positive impact on surgical treatment decision-making participation among lung cancer patients. When patients are more informed, they have a deeper understanding of their condition and are more likely to actively participate in medical decision-making. This was confirmed in a study by Cao Yayu et al. ( 26 ), which explored the participation of kidney transplant recipients in surgical decision-making and reached a similar conclusion. The extent of patient awareness largely depends on the quality of communication between healthcare providers and patients. According to the theory of communication and health outcomes, high-quality medical decision-making should have the following characteristics: ( 1 ) eliciting the patient's true needs; ( 2 ) presenting relevant clinical reports and information in a way that the patient can understand; ( 3 ) fully addressing the patient's emotional needs; and ( 4 ) ensuring that the views of clinical doctors and patients are aligned to make clinical decisions consistent with the patient's values and preferences ༈27༉. Therefore, during communication with patients and their families, healthcare professionals should actively encourage patients to take the initiative to understand their condition and, when patients are hesitant to participate in decision-making, adopt a patient-centered approach to encourage their active involvement in medical decisions. In this way, patients can gain a clearer and more accurate understanding of their condition and make wiser and more suitable treatment choices. 4.2.2 Mesosystem: No Significant Factors Identified The independent variables in the mesosystem (household registration type, employment status, and per capita family income level) did not show significant results in the correlation and regression analyses. We believe this may be due to limitations such as the sample size and sampling methods in this study. It is also possible that these factors may have an indirect impact on patient participation in medical decision-making. Future research could further explore the mediating role of family economic status in patients' surgical decision-making behavior. 4.2.3 Macrosystem: Commercial Insurance Has a Positive Impact on Decision-Making Participation This study found that patients with commercial insurance are more proactive in the surgical decision-making process. This may be because the costs of treatment and rehabilitation, as well as various medical examinations, can impose a heavy financial burden on patients, increasing their difficulties and hesitations when choosing treatment options ( 28 ). Patients with commercial insurance, having greater financial security, are more confident and proactive in participating in surgical decision-making. Against the backdrop of deepening medical system reform, commercial health insurance, as an essential and indispensable part of the multi-level medical security system ༈29༉, is rapidly developing and enhancing its capacity for coverage. Commercial insurance is forming a complementary relationship with basic medical insurance, working together to provide support. By offering financial security, commercial insurance may reduce patients' worries about the costs of surgical treatment, thereby increasing their confidence and motivation to actively participate in medical decision-making. Therefore, medical institutions and insurance companies can further strengthen cooperation, and when providing services and designing products, they should consider how to effectively communicate and explain to patients the scope and specific terms of their insurance coverage to better support patients' active and proactive involvement in medical decision-making. Additionally, the type of basic medical insurance did not yield significant conclusions regarding patient decision-making participation in this study, which may be related to the current design and implementation of medical insurance policies. Future research could further explore the specific mechanisms by which medical insurance policies affect patients' surgical decision-making behavior. 5. Conclusion The methods of surgical decision-making participation by patients with stages I to IIIa NSCLC are influenced by age, awareness level, and type of insurance. Many previous studies have found the potential benefits of patient participation in decision-making, such as improving patient satisfaction and health outcomes( 30 )༈31༉. Against the background of traditional paternalistic medical decision-making, there are still many patients who use passive decision-making methods, and the proportion of patients inclined to proactive decision-making is the smallest. It can be seen that doctors still dominate in diagnostic and treatment decision-making, and there is great development space for the implementation of shared decision-making models. To improve the informed participation of this population in surgical decision-making, medical staff should provide information support in a simple and understandable manner, and introduce auxiliary decision-making tools (multimedia, small programs, etc.) when necessary; at the same time, through multi-channel education, such as social media and patient salons, increase patients' understanding of disease knowledge, reduce fear and negative emotions, thereby optimizing the medical decision-making process. This study still has some limitations. First, the survey subjects are limited to a single hospital, which may affect the representativeness and generalizability of the results; in addition, this study mainly focuses on the types of surgical treatment decision-making participation and does not delve into the impact of different treatment decisions on patient treatment effects and satisfaction. Future research can increase the sample size and explore the changes in patients' participation in decision-making at different stages of hospitalization and treatment, providing more comprehensive and in-depth guidance for the decision-making of lung cancer patients' surgical treatment. The surgical decision-making participation of patients with Stage I to IIIA non-small cell lung cancer (NSCLC) is influenced by age, level of awareness, and insurance type. Many previous studies have identified the benefits of patient participation in decision-making, such as increased patient satisfaction and improved health outcomes ( 30 )༈31༉. However, in the context of traditional paternalistic medical decision-making, a significant number of patients still adopt a passive decision-making approach, with the smallest proportion preferring active decision-making. This indicates that doctors still dominate the diagnostic and treatment decision-making process, and there is considerable room for implementing shared decision-making models. To enhance the informed participation of this patient group in surgical decision-making, healthcare professionals can provide information support in an easy-to-understand manner at the micro level and introduce decision aids (such as multimedia and mini-programs) when necessary. Additionally, through multi-channel education, such as social media and patient salons, patients' understanding of their disease can be increased, reducing fear and negative emotions, thereby optimizing the medical decision-making process. At the macro level, medical institutions can strengthen cooperation with insurance companies to design and popularize commercial insurance products that meet patients' needs. This study still has some limitations. First, the survey was conducted in a single hospital, which may affect the representativeness and generalizability of the results. Second, the study mainly focused on the types of surgical treatment decision-making participation and did not delve into the impact of different treatment decisions on patient outcomes and satisfaction. Future research can further increase the sample size and explore how patient participation in decision-making changes at different stages, such as hospitalization and treatment, to provide more comprehensive and in-depth guidance for surgical treatment decision-making in lung cancer patients. Abbreviations NSCLC non-small cell lung cancer CPS Control Preference Scale Declarations This paper was supported by the Shanghai "Medical Rising Star" Young Medical Talent Training and Funding Program. Ethics approval and consent to participate In this study, the relevant data of human diseases are involved. These data are studied in strict accordance with the Declaration of Helsinki. This study has been approved by the Ethics Committee of Ruijin Hospital Shanghai Jiao Tong University School of Medicine(project number:(2023. No. 15). The study informed consent was obtained from all participants. Consent for publication: Not Applicable Author Contribution P. and D. wrote the main manuscript text, H. collected and analyzed the data, and Z. made revisions and translations to the manuscript. All authors reviewed the manuscript. Acknowledgements: Not Applicable Questionnaire Neither the Control Preference Scale nor the Patient’s Knowledge of Medical Decisions Survey Form was developed for the present study. Both instruments have been previously published and are cited in the manuscript. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Zheng RS, Chen R, Han BF, et al. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Jan, 2026 Reviewers agreed at journal 30 Jul, 2025 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 28 Jul, 2025 Submission checks completed at journal 27 Jul, 2025 First submitted to journal 27 Jul, 2025 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-7019179","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492001175,"identity":"77d71079-03ac-40f4-aca2-157b802ee0c0","order_by":0,"name":"Jiajie Pu","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiajie","middleName":"","lastName":"Pu","suffix":""},{"id":492001176,"identity":"87a38778-20ce-4293-ba8c-7aa0add840c6","order_by":1,"name":"Yanxia Hu","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yanxia","middleName":"","lastName":"Hu","suffix":""},{"id":492001177,"identity":"a6cb8433-046b-4a8e-8336-099a8923951b","order_by":2,"name":"Changying Zhang","email":"","orcid":"","institution":"Minhang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Changying","middleName":"","lastName":"Zhang","suffix":""},{"id":492001178,"identity":"c8a37bc2-e5d2-4f93-896a-730bab25541a","order_by":3,"name":"Zhengchuan Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3RsQrCMBCA4StCu1x1veJLBArFoW/iEpe6WOcOopk6FZx9C0fHSKFT1AdwKRSc6+5gdHIKcRPMv2TJB7kcgMv1i0kYtLMHbbYAmTXx2V1MvJ34gkC0E4W319ZODC91G4cHGsRXeWthlU4hOEkjiWTDulCRn5z5nEGT5QKX3EjYsYIYfcJEQUaeqHNByMykRhhrQnFlTRrU45fEGNqSSPkJ6xVxej2M61lKXJiJ/rGu5cWajyrMqF+l+TZQZvIRcuD6sNzOu0B+cdnlcrn+qSf8bENIV+zMWgAAAABJRU5ErkJggg==","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Zhengchuan","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2025-07-01 10:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7019179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7019179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87916546,"identity":"c622258b-9bb1-4fff-acf1-0b889519f0d0","added_by":"auto","created_at":"2025-07-30 11:04:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":970340,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7019179/v1/71efba6e-e608-4efd-847f-7ed7b52f8e2e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Study on the Participation of Lung Cancer Patients in Surgical Decision-Making and Its Influencing Factors from the Perspective of Ecological Systems Theory","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to the 2022 epidemiological analysis of malignant tumors in China, lung cancer still ranks first in both incidence and mortality among major tumors(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). From the perspective of pathology and treatment, lung cancer can be mainly divided into two major categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), the proportion of non-small cell carcinoma is relatively higher, accounting for more 80%~85%༈2༉. The treatment methods for lung cancer include surgical resection, chemotherapy, targeted therapy, neoadjuvant immunotherapy, radiotherapy, and palliative treatment, etc༈3༉. Among the commonly used treatment methods, anatomic pulmonary surgery resection remains an important clinical method for early to mid-stage lung cancer༈2༉, and the 5-year survival rate of early-stage NSCLC patients after surgical resection is generally between 70% and 80%༈4༉.\u003c/p\u003e\u003cp\u003eAmong the various medical methods, timing and correct method are crucial for the prognosis and healing progression of patients. Therefore, the importance of clinical medical decision-making is becoming increasingly prominent. It refers to a process of determining who should choose which medical intervention and when the intervention begins to select the most appropriate medical intervention measures(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Looking back at the history of medical development in Western countries, with the deepening understanding of doctors' authority and patients' rights, the medical decision-making model has undergone three major renovations: from the paternalistic decision-making model to the informed decision-making model, and further into the shared decision-making model༈7༉.\u003c/p\u003e\u003cp\u003eAlthough the shared decision-making model has been widely applied in the medical field, such as in chronic disease care and cancer treatment(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), however, influenced by the Confucian tradition, there is a significant difference between China's medical decision-making model and that of the West. Specifically, doctors usually first inform the patient's family of the diagnosis, and then the family members discuss together whether to inform the patient and how to choose the appropriate treatment plan༈9༉. Li Yu༈10༉ and others mentioned in the decision-making of liver cancer surgery that patients are in a passive position in informed consent and their actual participation in surgical decision-making is less than their expectations. Yan Caidie༈11༉ and others pointed out that most of Chinese gynecological tumor patients are passive in surgical decision-making.\u003c/p\u003e\u003cp\u003eThere are relatively few studies on the surgical decision-making of patients with stages I to IIIa NSCLC, and this patient group's surgical treatment decision-making participation, information access channels, and decision satisfaction are worth exploring. When analyzing the surgical decision-making behavior of lung cancer patients, we can understand the underlying driving factors from a broader perspective. Based on the Ecological Systems Theory, an individual's behavior and decision-making do not exist in isolation but are influenced by micro, meso, and macro systems.Therefore,this study aims to adopt the Ecological Systems Theory as the perspective and use a cross-sectional survey to comprehensively analyze the current status of surgical treatment decision-making participation among patients with Stage I to IIIA non-small cell lung cancer (NSCLC). The study will take the patients' microsystem (educational level, presence of comorbid conditions, degree of financial burden related to medical care, and extent of awareness of medical decision-making), mesosystem (household registration type, employment status, and per capita family income level), and macrosystem (type of basic medical insurance and commercial insurance status) as the hypothetical independent variables, and explore the associations between these variables and surgical treatment decisions. This will lay a scientific foundation for optimizing the decision-making process and improving patient satisfaction, and also provide targeted guidance for healthcare professionals to encourage active patient participation in decision-making, ultimately enhancing decision quality and treatment outcomes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Research Objects\u003c/h2\u003e\u003cp\u003eFrom February 2024 to June 2024, convenience sampling was used to select 219 postoperative lung cancer patients in the Department of Thoracic Surgery of a tertiary general hospital as research objects. Inclusion criteria: aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years; diagnosed with stage I to IIIa lung malignant tumors according to NCCN guidelines(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e); the patient has consented about the condition; has undergone surgical treatment; consented and voluntarily participated in this study. Exclusion criteria: individuals with metastatic tumors; patients with mental illness, cognitive dysfunction, communication disorders, and other severe physical illnesses. This study has been approved by the Ethics Committee of Ruijin Hospital Shanghai Jiao Tong University School of Medicine.This research strictly followed the principles stated in the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Research Methods\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Sample Size Calculation\u003c/h2\u003e\u003cp\u003eSince the surgical decision-making type investigated in this study is a categorical variable, so a single proportion estimation method was considered for sample size calculation. Referring to the maximum proportion of 43.4% in three categories from previous literature(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), α=0.05, allowable error δ is 5%, and the required sample size was calculated to be 213 cases using PASS software. Considering a non-response rate of 10%, a planned sample of 234 cases was included. A total of 234 questionnaires were distributed, and 219 valid questionnaires were collected, with a valid recovery rate of 93.6%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Research Tools\u003c/h2\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) General information survey, including gender, education level, marital status, household registration type, employment status, basic medical insurance type, whether holding commercial insurance, whether having underlying diseases, degree of medical economic burden, and family per capita income level.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Control Preference Scale(CPS) was originally developed by Canadian scholars Degner et al.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and translated and processed by Xu Xiaolin et al.༈15༉. In this study, we used this scale to assess the actual participation type of lung cancer patients in the surgical decision-making process. The scale consists of 5 options from A to E, where A means the doctor made the decision entirely; B means the doctor made the decision after seriously considering patient\u0026rsquo;s thoughts; C means the patient and the doctor made the decision together after comprehensive consideration; D means the patient made the decision after seriously considering the doctor's advice; E means the patient made the decision after understanding all medical choices. Choosing A or B is considered passive decision-making; choosing C is considered shared decision-making; choosing D or E is considered proactive decision-making. This decision scale is simple and clear, easy to fill out, time-saving, and can quickly and effectively assess the patient's surgical decision-making tendency. The scale language is easy to understand, facilitating patient comprehension and improving data collection efficiency. The scale's Cronbach's α coefficient is 0.899, and due to its high reliability, it is often used in domestic and foreign related research༈15,16\u0026ndash;18༉.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Patient's Knowledge of Medical Decisions Survey Form, developed by Xu Xiaolin(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), is used to measure the lung cancer patients' knowledge of surgical decisions, with the following 5 questions: ① The benefits and risks of choosing treatment versus non-treatment; ② What treatment options are available; ③ The benefits of the chosen treatment plan and the expected results; ④ The potential risks of the chosen treatment plan; ⑤ The expected situation during the implementation of the chosen treatment plan. This study uses the Likert 5-point scoring method for quantification, with specific levels being: \"Very Unfamiliar\" (1 point), \"Not Very Familiar\" (2 points), \"Moderately Familiar\" (3 points), \"Fairly Familiar\" (4 points), and \"Very Familiar\" (5 points). The total score range is set from 5 to 25 points, with higher scores indicating a higher level of knowledge. Additionally, the Cronbach's α coefficient for this study is 0.806, showing good reliability.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3 Data Collection Method\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData was collected within 5 days after the patient's surgery using an offline anonymous questionnaire. Before the distribution of each questionnaire, the researcher or interviewer explained the purpose of the study to the patients in detail and obtained their informed consent before distributing the questionnaire. During the filling process, the researcher or interviewer used united guidance and answered any questions the patients had. After the patients completed the questionnaire, it was collected on the spot.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4 Statistical Processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSPSS 25.0 statistical software was used. Quantitative data is represented by x\u0026thinsp;\u0026plusmn;\u0026thinsp;s or M(Q1, Q3); count data is represented by the number of cases and percentage. The correlation between ordinal variables or rank variables was analyzed using Spearman's rank correlation analysis. In single-factor analysis and correlation analysis, variables with statistical significance were selected, and a multifactor logistic regression analysis model was established based on these variables. A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 General Information of Research Subjects\u003c/h2\u003e\u003cp\u003eA total of 219 patients with stages I to IIIa NSCLC, with an average age of 52.68\u0026thinsp;\u0026plusmn;\u0026thinsp;13.64 years, and other data is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eGeneral Information of Research Subjects (n\u0026thinsp;=\u0026thinsp;219)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClassification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of Cases (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary School and below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJunior High School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh School/Technical School/Vocational School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollege\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBachelor's Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaster's Degree and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivorced and Widowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHousehold Registration Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEmployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnemployed and Not Retired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eBasic Medical Insurance Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShanghai Employee Medical Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShanghai Resident Medical Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInter-regional Medical Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Social Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHolding Commercial Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUnderlying Diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eMedical Economic Burden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery Heavy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeavier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLighter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eFamily Per Capita Income Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3000 and below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3000\u0026ndash;5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5000\u0026ndash;10000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Types of Surgical Treatment Decision-Making Participation by Lung Cancer Patients\u003c/h2\u003e\u003cp\u003eAmong the types of participation in surgical treatment decision-making for lung cancer patients, passive decision-making accounted for 86 cases (39.3%), shared decision-making for 81 cases (37.0%), and proactive decision-making for 52 cases (23.7%). For more details, see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTypes of Surgical Treatment Decision-Making Methods for Lung Cancer Patients (n\u0026thinsp;=\u0026thinsp;219)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClassification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of Cases (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePassive Decision-Making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOption A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOption B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShared Decision-Making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOption C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eProactive Decision-Making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOption D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOption E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Correlation Analysis of Types of Surgical Treatment Decision-Making Participation by Lung Cancer Patients\u003c/h2\u003e\u003cp\u003eThe results of Spearman's rank correlation analysis indicate that there is a significant negative correlation between the participation types of lung cancer patients in the surgical treatment decision-making process and their age (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and whether they have commercial insurance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), meanwhile there is a positive correlation with educational level (P\u0026thinsp;=\u0026thinsp;0.001), chosen surgical method (P\u0026thinsp;=\u0026thinsp;0.017), and awareness of medical decision-making (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eCorrelation Analysis of Types of Surgical Treatment Decision-Making Participation by Lung Cancer Patients (n\u0026thinsp;=\u0026thinsp;219)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.396**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.214**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Registration Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic Medical Insurance Reimbursement Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommercial Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.227**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderlying Diseases (Hypertension, Diabetes, etc.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Economic Burden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily Per Capita Monthly Income Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst Hospitalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst Lung Surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall Assessment of Own Health Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChosen Surgical Method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.162*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAwareness of Medical Decision-Making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.563**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e*Note: *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Multiple Logistic Regression Analysis of Types of Surgical Treatment Decision-Making Participation by Lung Cancer Patients\u003c/h2\u003e\u003cp\u003eTaking the participation type in lung cancer patients' surgical treatment decision-making as the dependent variable (passive decision-making\u0026thinsp;=\u0026thinsp;1, shared decision-making\u0026thinsp;=\u0026thinsp;2, proactive decision-making\u0026thinsp;=\u0026thinsp;3; using passive decision-making as control), and using the items with statistical significance in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e as independent variables (continuous variables are entered as original values; dummy variables are assigned values. Commercial insurance: 1\u0026thinsp;=\u0026thinsp;Yes, 2\u0026thinsp;=\u0026thinsp;No), multiple logistic regression analysis was performed with α entry\u0026thinsp;=\u0026thinsp;0.05 and α exit\u0026thinsp;=\u0026thinsp;0.10. The results show that the variables entering the regression equation are age, awareness of medical decision-making, and whether there is commercial insurance, see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple Logistic Regression Analysis of Types of Surgical Treatment Decision-Making Participation by Lung Cancer Patients (n\u0026thinsp;=\u0026thinsp;219)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDependent Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003evarible\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWaldχ༒\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUpper limit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLower limit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eProactive Decision-Making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.07**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAwareness of Medical Decision-Making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.634**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.299\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCommercial Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.325*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.599\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eShared Decision-Making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.044*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.984\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAwareness of Medical Decision-Making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.138**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCommercial Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.841*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.254\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e*Note: *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Current Status of Lung Cancer Patients' Participation in Surgical Treatment Decision-Making\u003c/h2\u003e\u003cp\u003eThis study explored the current status of lung cancer patients' participation in surgical treatment decision-making and conducted a multivariate logistic regression analysis of its influencing factors. The results show that among lung cancer patients, the proportions of passive decision-making, shared decision-making, and proactive decision-making are 39.3%, 37.0%, and 23.7%, respectively. This result differs significantly from Zhang Jina's(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) study on non-small cell lung cancer patients' surgical decision-making types, which reported 67.3% passive decision-making. It is evident that although a considerable number of patients play a relatively passive role in the treatment process, the increased proportions of shared decision-making (37.0%) and proactive decision-making (23.7%) indicate that more patients are beginning to take the initiative in treatment decision-making. Possible reasons include: ① The two studies were conducted in different regions, and the patient groups are in different social environments and cultural backgrounds; ② With the popularization of medical knowledge and the improvement of patients' education levels, patients pay more attention to their health status, have more understanding and knowledge of diseases and treatment plans, and are more willing to participate in the decision-making process; ③ The diversification of patients' medical information acquisition channels enables patients to participate more actively in treatment decision-making; ④ There is a 6-year gap between the two studies, and in recent years, medical system reforms, such as promoting patient-centered medical service models and encouraging patient participation in decision-making, have increased patients’ involvement; ⑤ Compared to previous studies, this study has a larger sample size and may more accurately reflect the current actual situation of lung cancer patients' participation in surgical treatment decision-making.\u003c/p\u003e\u003cp\u003eThe degree of passive decision-making among thoracic surgery patients (39.3%) is close to that of general surgery patients (43.40%)(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and neurosurgery stroke surgery patients (44.44%)༈21༉ in previous domestic studies but significantly different from foreign studies using the Decision Expectancy Scale (chronic kidney disease patients: 8%༈22༉; hand surgery patients: 22%༈23༉). This may be due to various factors such as culture, education level, health literacy, and medical systems between China and other countries, and it also indicates that the initiative and enthusiasm of Chinese patients in treatment decision-making need to be strengthened.\u003c/p\u003e\u003cp\u003e\u003cb\u003e4.2 Factors Influencing Surgical Treatment Decision-Making Participation in Lung Cancer Patients from the Perspective of Ecological Systems Theory\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the Ecological Systems Theory, individual behaviors and decisions are influenced by the micro, meso, and macro systems. This study analyzes the factors influencing surgical treatment decision-making participation among lung cancer patients within the framework of Ecological Systems Theory.\u003c/p\u003e\u003cp\u003e4.2.1 Microsystem: The Impact of Individual Characteristics on Decision-Making Participation\u003c/p\u003e\u003cp\u003eAmong the variables in the microsystem, this study found that age has a negative impact on the surgical treatment decision-making participation of lung cancer patients. That is, the older the patient, the more passive their decision-making participation. This is similar to the findings of a study on medical decision-making types among adults and another on medical decision-making types among prostate cancer patients (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The increase in age may imply a decline in the ability to obtain medical information, as well as the capacity to understand and accept new things and technologies ༈25༉. Against the backdrop of building age-friendly hospitals, it is essential to pay more attention to the needs and difficulties of elderly patients in medical decision-making, and to provide more humanized services and supportive tools to help them better understand treatment options and related risks, thereby enhancing their participation in decision-making. In medical practice, doctors and nurses should provide more support and guidance to elderly patients, offering more easy-to-understand educational videos and communication strategies to improve their decision-making participation.\u003c/p\u003e\u003cp\u003eAnother key variable in the microsystem is the degree of awareness of medical decision-making, which has a positive impact on surgical treatment decision-making participation among lung cancer patients. When patients are more informed, they have a deeper understanding of their condition and are more likely to actively participate in medical decision-making. This was confirmed in a study by Cao Yayu et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), which explored the participation of kidney transplant recipients in surgical decision-making and reached a similar conclusion. The extent of patient awareness largely depends on the quality of communication between healthcare providers and patients. According to the theory of communication and health outcomes, high-quality medical decision-making should have the following characteristics: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) eliciting the patient's true needs; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) presenting relevant clinical reports and information in a way that the patient can understand; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) fully addressing the patient's emotional needs; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) ensuring that the views of clinical doctors and patients are aligned to make clinical decisions consistent with the patient's values and preferences ༈27༉. Therefore, during communication with patients and their families, healthcare professionals should actively encourage patients to take the initiative to understand their condition and, when patients are hesitant to participate in decision-making, adopt a patient-centered approach to encourage their active involvement in medical decisions. In this way, patients can gain a clearer and more accurate understanding of their condition and make wiser and more suitable treatment choices.\u003c/p\u003e\u003cp\u003e4.2.2 Mesosystem: No Significant Factors Identified\u003c/p\u003e\u003cp\u003eThe independent variables in the mesosystem (household registration type, employment status, and per capita family income level) did not show significant results in the correlation and regression analyses. We believe this may be due to limitations such as the sample size and sampling methods in this study. It is also possible that these factors may have an indirect impact on patient participation in medical decision-making. Future research could further explore the mediating role of family economic status in patients' surgical decision-making behavior.\u003c/p\u003e\u003cp\u003e4.2.3 Macrosystem: Commercial Insurance Has a Positive Impact on Decision-Making Participation\u003c/p\u003e\u003cp\u003eThis study found that patients with commercial insurance are more proactive in the surgical decision-making process. This may be because the costs of treatment and rehabilitation, as well as various medical examinations, can impose a heavy financial burden on patients, increasing their difficulties and hesitations when choosing treatment options (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Patients with commercial insurance, having greater financial security, are more confident and proactive in participating in surgical decision-making. Against the backdrop of deepening medical system reform, commercial health insurance, as an essential and indispensable part of the multi-level medical security system ༈29༉, is rapidly developing and enhancing its capacity for coverage. Commercial insurance is forming a complementary relationship with basic medical insurance, working together to provide support. By offering financial security, commercial insurance may reduce patients' worries about the costs of surgical treatment, thereby increasing their confidence and motivation to actively participate in medical decision-making. Therefore, medical institutions and insurance companies can further strengthen cooperation, and when providing services and designing products, they should consider how to effectively communicate and explain to patients the scope and specific terms of their insurance coverage to better support patients' active and proactive involvement in medical decision-making.\u003c/p\u003e\u003cp\u003eAdditionally, the type of basic medical insurance did not yield significant conclusions regarding patient decision-making participation in this study, which may be related to the current design and implementation of medical insurance policies. Future research could further explore the specific mechanisms by which medical insurance policies affect patients' surgical decision-making behavior.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe methods of surgical decision-making participation by patients with stages I to IIIa NSCLC are influenced by age, awareness level, and type of insurance. Many previous studies have found the potential benefits of patient participation in decision-making, such as improving patient satisfaction and health outcomes(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)༈31༉. Against the background of traditional paternalistic medical decision-making, there are still many patients who use passive decision-making methods, and the proportion of patients inclined to proactive decision-making is the smallest. It can be seen that doctors still dominate in diagnostic and treatment decision-making, and there is great development space for the implementation of shared decision-making models. To improve the informed participation of this population in surgical decision-making, medical staff should provide information support in a simple and understandable manner, and introduce auxiliary decision-making tools (multimedia, small programs, etc.) when necessary; at the same time, through multi-channel education, such as social media and patient salons, increase patients' understanding of disease knowledge, reduce fear and negative emotions, thereby optimizing the medical decision-making process. This study still has some limitations. First, the survey subjects are limited to a single hospital, which may affect the representativeness and generalizability of the results; in addition, this study mainly focuses on the types of surgical treatment decision-making participation and does not delve into the impact of different treatment decisions on patient treatment effects and satisfaction. Future research can increase the sample size and explore the changes in patients' participation in decision-making at different stages of hospitalization and treatment, providing more comprehensive and in-depth guidance for the decision-making of lung cancer patients' surgical treatment.\u003c/p\u003e\u003cp\u003eThe surgical decision-making participation of patients with Stage I to IIIA non-small cell lung cancer (NSCLC) is influenced by age, level of awareness, and insurance type. Many previous studies have identified the benefits of patient participation in decision-making, such as increased patient satisfaction and improved health outcomes (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)༈31༉. However, in the context of traditional paternalistic medical decision-making, a significant number of patients still adopt a passive decision-making approach, with the smallest proportion preferring active decision-making. This indicates that doctors still dominate the diagnostic and treatment decision-making process, and there is considerable room for implementing shared decision-making models.\u003c/p\u003e\u003cp\u003eTo enhance the informed participation of this patient group in surgical decision-making, healthcare professionals can provide information support in an easy-to-understand manner at the micro level and introduce decision aids (such as multimedia and mini-programs) when necessary. Additionally, through multi-channel education, such as social media and patient salons, patients' understanding of their disease can be increased, reducing fear and negative emotions, thereby optimizing the medical decision-making process. At the macro level, medical institutions can strengthen cooperation with insurance companies to design and popularize commercial insurance products that meet patients' needs.\u003c/p\u003e\u003cp\u003eThis study still has some limitations. First, the survey was conducted in a single hospital, which may affect the representativeness and generalizability of the results. Second, the study mainly focused on the types of surgical treatment decision-making participation and did not delve into the impact of different treatment decisions on patient outcomes and satisfaction. Future research can further increase the sample size and explore how patient participation in decision-making changes at different stages, such as hospitalization and treatment, to provide more comprehensive and in-depth guidance for surgical treatment decision-making in lung cancer patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enon-small cell lung cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCPS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eControl Preference Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis paper was supported by the Shanghai \u0026quot;Medical Rising Star\u0026quot; Young Medical Talent Training and Funding Program.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eIn this study, the relevant data of human diseases are involved. These data are studied in strict accordance with the Declaration of Helsinki. This study has been approved by the Ethics Committee of Ruijin Hospital Shanghai Jiao Tong University School of Medicine(project number:(2023. No. 15). The study informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eP. and D. wrote the main manuscript text, H. collected and analyzed the data, and Z. made revisions and translations to the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eQuestionnaire\u003c/h2\u003e\n\u003cp\u003eNeither the Control Preference Scale nor the Patient\u0026rsquo;s Knowledge of Medical Decisions Survey Form was developed for the present study. Both instruments have been previously published and are cited in the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZheng RS, Chen R, Han BF, et al. Analysis of the incidence and mortality of malignant tumors in China in 2022. Chin J Oncol. 2024;46(3):221\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Health Commission of the People's Republic of China. Guidelines for the diagnosis and treatment of primary lung cancer (2022 edition). China Ration Drug Use. 2022;19(9):1\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Comprehensive Cancer Network. (2021). NCCN Clinical Practice Guidelines in Oncology: Non-Small Cell Lung Cancer, Version 5.2021. 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A mixed path study on medical trust and decision-making participation of cancer patients in the context of doctor-shopping. Dissertation, Beijing Jiaotong University, Beijing.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo Y, Han LL, Wang J, et al. Analysis of the current situation and influencing factors of decisional conflict in surgical method decision-making for breast cancer patients. Chin Gen Practical Nurs. 2024;22(8):1560\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo MJ, Li YZ, Liu Y. Analysis of the social and commercial integration model of China's inclusive insurance development and suggestions for holistic governance. Chin J Hosp Adm. 2023;39(7):541\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurtzman ET, Greene J. Effective presentation of health care performance information for consumer decision making: A systematic review. Patient Education \u0026amp; Counseling; 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpstein MR. Whole mind and shared mind in clinical decision-making. Patient Educ Couns. 2013;90(2):200\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lung cancer, Decision-making participation, Decision-making expectation, Influencing factors","lastPublishedDoi":"10.21203/rs.3.rs-7019179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7019179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim.\u003c/strong\u003e To investigate the surgical decision-making and its influencing factors in patients with stages I to IIIa non-small cell lung cancer (NSCLC) under Ecological Systems Theory.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e Convenience sampling was used to select 219 surgical patients with stages I to IIIa NSCLC admitted to a tertiary hospital from February 2024 to June 2024, and an electronic questionnaire survey was distributed. The questionnaire included a general information survey, a Control Preference Scale (CPS), and a survey on patients' level of knowledge about medical decisions. Spearman's rank correlation analysis and multiple logistic regression analysis were used to explore the types of surgical decision-making participation and their influencing factors in patients with stages I to IIIa NSCLC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e Among the types of participation in surgical treatment decision-making for lung cancer patients, passive decision-making was the most common. Correlation analysis revealed that the type of participation in surgical treatment decision-making was negatively correlated with age and having commercial insurance (both P \u0026lt; 0.01), and positively correlated with educational level, chosen surgical method, and medical decision-making awareness (all P \u0026lt; 0.05). Multiple logistic regression analysis found that age, level of medical decision-making knowledge, and the presence of commercial insurance were influencing factors for the type of surgical decision-making participation (all P \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e. Patients who are younger, more informed about medical decisions, and hold commercial insurance are more inclined to adopt shared decision-making or proactive decision-making. Based on the Ecological Systems Theory, at the micro level, healthcare professionals should actively provide targeted decision-making support to promote patients' deeper involvement in surgical decision-making, thereby improving decision quality and health outcomes. At the macro level, medical institutions can strengthen cooperation with insurance companies to design and popularize commercial insurance products that meet patients' needs.\u003c/p\u003e","manuscriptTitle":"A Study on the Participation of Lung Cancer Patients in Surgical Decision-Making and Its Influencing Factors from the Perspective of Ecological Systems Theory","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 10:56:26","doi":"10.21203/rs.3.rs-7019179/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-19T06:24:17+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"167874709899340250127108444347742814525","date":"2025-07-30T08:21:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T08:14:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-28T06:48:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-27T08:17:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2025-07-27T06:39:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"146828fa-cebf-49f3-9acc-62e0ae38b6c1","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T20:53:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 10:56:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7019179","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7019179","identity":"rs-7019179","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}