Translation, Adaptation, and Validation of Person-Centered Primary Care Measures for Patients in Family Doctor Contract Services within Mainland China

Translation, Adaptation, and Validation of Person-Centered Primary Care Measures for Patients in Family Doctor Contract Services within Mainland China | 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 Translation, Adaptation, and Validation of Person-Centered Primary Care Measures for Patients in Family Doctor Contract Services within Mainland China Yang Wang, Dehua Yu, Hua Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4120806/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Mar, 2025 Read the published version in BMC Primary Care → Version 1 posted 4 You are reading this latest preprint version Abstract Background: In the context of China's health reforms enhancing its primary care function through Family Doctor Contract Service Program, effectively measuring its health-beneficial features is paramount. This study endeavors to translate, adapt, and validate the Person-Centered Primary Care Measure (PCPCM) for primary care patients enrolled in family doctor contract services in mainland China. Methods Following the guidelines by Sousa and Rojjanasrirat, we translated and adapted the PCPCM into simplified Chinese and evaluated its psychometric properties. Our assessment involved 583 patients in family doctor contract services from 10 primary care facilities in Shanghai, China. We analyzed various aspects, including internal consistency, stability, homogeneity, construct-related validity, criterion-related validity, dimensionality, and model fit of the adapted PCPCM. Additionally, we conducted subgroup analyses focusing on patients with hypertension and/or diabetes and seniors aged 65 and above. Results: The adaptation resulted in the PCPCM for patients under the family doctor contract service program(PCPCM-SC-FDCP), tailored for primary care patients under the family doctor contract service program in mainland China. Initial pilot testing led to refinements for clearer applicability, particularly for Item 5. The PCPCM-SC-FDCP demonstrated excellent internal consistency (Cronbach's α = 0.94), homogeneity (Correlation = 0.67–0.85), construct-related validity (Correlation = 0.72, p < 0.001), and criterion-related validity (Correlation = 0.54, p < 0.001), alongside satisfactory dimensionality and model fit. Stability reliability (ICC = 0.56), while slightly below the ideal, was deemed acceptable. The instrument also performed well in subgroup analyses. Conclusions: The PCPCM-SC-FDCP proves to be an effective patient-reported outcome measure, for measuring patient experiences with primary care's functional features among those enrolled in family doctor contract services in mainland China. Its widespread adoption is anticipated to significantly aid the strategic reform of China's primary care system by highlighting and improving functional features within the local healthcare framework. Primary Care Quality measurement Quality improvement Patient Reported Outcome Measure China Figures Figure 1 Introduction Primary care is pivotal in achieving enhanced outcomes in both population health and health equity, while simultaneously ensuring cost-effectiveness, as opposed to a specialist-centric healthcare 1,2 . Its efficacy is inherently tied to its distinctive functional features within healthcare services, such as accessibility, continuity, comprehensiveness, and coordination 2,3 . This highlights the critical importance of effective measuring these features to evaluate their performance and quality effectively 4 . To achieve this, various instruments have been developed and empirically validated in different countries. Notable among these are the Person-Centered Primary Care Measure (PCPCM), the Primary Care Assessment Tool (PCAT), and the Quality and Outcomes Framework (QOF) 5 . A major challenge in assessing the functional features of primary care in Mainland China is its unique historical and societal context, intricately linked with a healthcare system undergoing reform. Established between the 1950s and 1970s, China's primary care initially aimed at providing accessible health services to rural and economically disadvantaged populations 6 . However, this focus shifted during the revenue-driven healthcare reforms of the 1970-1980s, which transitioned towards a disease-centric model of care 7 . Recently, China's primary care framework has evolved into a model significantly influenced by government-led planning and the structuring of healthcare institutions: despite an extensive network of nearly one million primary care facilities, which ensures the delivery of accessible health services to marginalized communities, the system's practice model and scope are less defined, especially when compared to the specialist outpatient clinics of general hospitals 8,9 . Moreover, the scope and quality of primary care are further compromised by a significant shortage of experienced practitioners, limited medical equipment, and a constrained range of pharmaceutical options 10 . Additionally, the lack of a primary care-led gatekeeping mechanism leads to a competitive landscape where secondary care providers vie with primary care facilities in a free market for routine clinical care. This competition poses significant challenges in establishing effective collaborative relationships 11 . Over the past decade, health system reforms in China have positioned the family doctor contract service program as a key policy initiative 12 . The core of this program is the formation of family doctor teams, which are essential for establishing long-term therapeutic relationships with local community residents who voluntarily contract with them. Each team can serve up to 2000 individuals and is centered around primary care facilities. These teams consist of primary care physicians, including general practitioners and community specialist doctors, and are supported by nurses, physician assistants, and public health doctors 13 . They provide a comprehensive array of primary care services to their contracted residents, including health consultations, routine physical examinations, diagnosis and treatment of common ailments, chronic disease management, and traditional Chinese medicine services 13 . Additionally, they assume responsibility for essential public health services in specific communities or villages, such as vaccinations, surveillance of certain infectious and non-communicable diseases, and preventive care for vulnerable populations like children, pregnant women, and the elderly 14 . In some regions, their clinical capacity has been enhanced through increased medical resources, such as expanded prescription capabilities and exclusive fast-track appointment slots, facilitating more efficient patient referrals to major hospitals 13 . With the government's goal to expand this program to at least 75% of China's population by 2035 15 , it demonstrates the potential to become a cornerstone of national primary care services with higher performance in mainland China in the future. In the evaluation of the functional features of China's primary care, particularly following the integration of the Family doctor contract service program, it becomes essential to use an assessment tool that is firmly rooted in the doctor-patient therapeutic relationship and remain cost-effective 16 . Moreover, this tool must be adaptable to the unique primary care environment of mainland China and capable of capturing patients' genuine perceptions of their beneficial experiences with primary care services 16 . We selected the PCPCM for its unparalleled ability to surpass other similar instruments: its eleven items succinctly capture the essential beneficial aspects of primary care, reflecting the direct experiences of the local patient population 17 . This framework allows for a more adaptable evaluation of varied patient responses to primary care across different social contexts and healthcare systems, unlike methods that gather responses from specific populations to fixed scenarios within a particular healthcare setting. Although the PCPCM has been translated and validated in various languages across 35 OECD countries and regions 18 , including Hong Kong 19,20 , China, the significant linguistic differences between traditional and simplified Chinese, along with the considerable disparities in the health systems of Hong Kong and mainland China, highlight the necessity for a version of the PCPCM specifically tailored for primary care patients who have enrolled in family doctor contract services in mainland China. Therefore, our study aims to translate, adapt, and validate the PCPCM in simplified Chinese to measure the patient experience within this population accurately. Method The original PCPCM consists of a question followed by 11 items that cover a spectrum of primary care functional features. Respondents are prompted to answer using a four-point Likert scale, with options ranging from "Definitely" to "Not at all” 17 . In this study, we adhered to the cross-cultural translation, adaptation, and validation guidelines proposed by Sousa and Rojjanasrirat 24 , employing a three-step process to develop a Simplified Chinese version of the PCPCM for patients under the family doctor contract service program (PCPCM-SC-FDCP). Development of pre-final version of PCPCM-SC-FDCP The initial phase in developing the PCPCM-SC-FDCP was grounded in a meticulous and culturally sensitive translation process. This phase involved two bilingual translators, both possessing fluency in English and Simplified Chinese. Their expertise extended beyond language proficiency; the first translator brought practical experience in general practice and public health, while the second translator was deeply familiar with the local primary care contexts and nuances of the family doctor contract service, although with a more limited background in health sciences. To ensure translation fidelity, these translators independently converted the PCPCM from English to Simplified Chinese. Special attention was given to aligning the translation with the specific needs and experiences of our target population—patients under family doctor contracts. Their independent translations were then scrutinized by a third bilingual translator, an active general practitioner in a Chinese primary care setting. This translator's role was pivotal in identifying and resolving inconsistencies between the two translations, focusing on terminology, sentence structure, conveyed meaning, and the suitability of the adaptations made. Through collaborative efforts, these translations were harmonized, culminating in a preliminary version of the PCPCM-SC-FDCP. Further refining the translation, two additional bilingual translators, both native English speakers studying in China, independently back-translated the preliminary version into English. One translator specialized in general practice, while the other, lacking a specialized background in health sciences, brought a different perspective. A comprehensive review session with all five translators was then organized. The insights gained from this session were instrumental in refining and finalizing the translation, culminating in the development of the pre-final version of the PCPCM-SC-FDCP. Pilot Testing of PCPCM-SC-FDCP The pilot testing of the pre-final versions of the PCPCM-SC-FDCP was conducted in two distinct phases. In the initial phase, the PCPCM-SC-FDCP was administered to a group of 20 primary care patients who were under contract with family doctors. These patients were reached through general practitioners in five provinces (Beijing, Chongqing, Henan, Liaoning, and Shanghai). The survey was conducted using an online questionnaire. They were requested to evaluate the clarity of the instructions, items, and response format of the translated scale using a straightforward dichotomous scale (clear or unclear). The advancement to the next phase of testing was contingent on achieving clarity as reported by over 80% of the participants, encompassing the instructions, response format, and individual items. Additionally, we assembled a panel of 10 experts from mainland China, all well-versed in the functional features of primary care. These experts were selected from general practice and public health departments within both practice and research institutes. Following their feedback, we made meticulous adjustments to the wording, proceeding only after more than 80% of the panel agreed that the instructions, response format, and each item were sufficiently clear to confirm conceptual equivalence. During the second phase, we engaged these ten experts to evaluate the content relevance of each item using a four-point scale, where 1=not relevant, 2=unable to assess relevance, 3=relevant but requiring minor alteration, and 4=very relevant and succinct. Items receiving a rating of 1 (not relevant) or 2 (unable to assess relevance) were further refined. To ensure robust content validity, we set a minimum threshold of Scale-Content Validity Index/Average (S-CVI/Ave) ≥ 0.90 24 . Psychometric test ing of PCPCM-SC-FDCP In Shanghai, China, we successfully recruited a total of 583 primary care patients under family doctor contract service program from 10 primary care facilities. These institutions were strategically selected across urban (4 facilities), suburban (3 facilities), and rural (3 facilities) settings to ensure diverse representation. Each institution enrolled between 50 to 100 patients. In accordance with established empirical standards, this sample size was considered adequate for the psychometric analysis of the eleven dimensions of the pre-final versions of the PCPCM-SC-FDCP 24 . The inclusion criteria included: (1) being aged 18 years or older; (2) cognitive alertness and ability to read or understand the relevant informed consent documents; (3) having utilized at least one primary care service from a family doctor in the past 12 months, consistent with the implementation recommendations for the target population set forth by the American Academy of Family Physicians 25 . As indicated in Table 1, we collected data intended to support psychometric analyses of the PCPCM-SC-FDCP. Additionally, we gathered data on participants' gender, age, educational level, mean annually income, EQ-5D utility scores, duration of family doctor contracts, and frequency of consultations with a family doctor in the past year. Using this data, we analyzed the internal consistency reliability, stability reliability, homogeneity, construct-related validity, criterion-related validity, dimensionality, and model fit of the PCPCM-SC-FDCP. We established adequate standards for these analyses based on relevant methodological guidelines 26-30 . Moreover, we conducted two subgroup analyses as sensitivity analysis, specifically focusing on individuals with diabetes and/or hypertension, and seniors aged 65 and above. These groups represent the primary and largest demographics for Family doctor contract service program in mainland China 15 . The statistical analyses were carried out using several software tools, including Stata 17.0 SE (StataCorp LLC, College Station, TX, USA), IBM SPSS Statistics V.26.0 (IBM Corp., Armonk, NY, USA), R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria), and Winsteps 3.72.3 (Winsteps.com, Beaverton, OR, USA). Ethics Approval and Consent to Participate Ethical approval for this study was granted by the ethics review board at Peking University, with the approval number IRB00001052-23077. In accordance with the permissions granted by the ethics committee, informed consent was obtained verbally from all participants. The participants were fully informed about the purpose of the study, their right to withdraw at any time without any consequences, and the measures taken to ensure their privacy and the confidentiality of their responses. Verbal consent was deemed appropriate by the ethics committee due to the nature of the study and was documented accordingly. Results Through the dedicated efforts of five translators, the PCPCM was effectively translated into Chinese (see Table S1). Feedback from 20 primary care patients under family doctor contract service program and 10 experts led to several revisions in the PCPCM-SC-FDCP. The most significant change was to item 5, concerning primary care's coordination function. Originally, it read 'My family doctor or the team could coordinate the care I get from multiple places.' This was revised to 'My family doctor or the team is aware of the medical services I receive from various institutions,' following insights from many experts (70%) and patients (50%). They noted that due to the lack of a robust gate-keeping system and formal referral mechanism in mainland China, primary care patients often lack experience with care coordination, making it challenging to evaluate their family doctor teams’ coordination capabilities (see Table S2). After revising the questionnaire and confirming that over 80% of evaluators found its content clear, we calculated theS-CVI/Ave for each PCPCM-SC-FDCP item based on 10 experts' responses. The S-CVI/Ave was 0.90, enabling us to move forward with further psychometric analysis (see Table S2). Our survey of 583 patients showed that 466 (79.93%) had hypertension or diabetes, and 411 (70.49%) were aged 65 or older. Sociodemographic characteristics are detailed in Table 2, including gender, age, education level, average annual income, and EQ-5D utility scores. Regarding family doctor services, most patients had long-term contracts and frequent visits within the last year. Table 3 presents patients' reported experiences with primary care. Survey results revealed very positive experiences among patients with family doctor contracts, indicated by the overall mean scores of the PCPCM-SC-FDCP and the final scores of the full version of the PCAT, as well as the high satisfaction rate with family doctor contract services. The PCPCM-SC-FDCP's psychometric performance, detailed in Table 4 and Figure 1, was excellent. Noteworthy were its internal consistency reliability (Cronbach's α=0.94), homogeneity (Correlation=0.67-0.85), construct-related validity (Correlation=0.72, p≤0.01), and criterion-related validity (Correlation=0.54, p≤0.01). Rasch analysis confirmed the model fit, with Outfit Mean Square and Infit Mean Square values close to 1. The PCPCM-SC-FDCP also exhibited unidimensionality, aligning its 11 items with the same concept. Stability reliability (ICC=0.56) was slightly below ideal but still acceptable. Targeted psychometric analyses on the two subgroups also demonstrated the PCPCM-SC-FDCP's good psychometric properties for these populations (see Table 4 and Figure 1). Discussion Through this study, we translated the PCPCM into a Simplified Chinese version, specifically tailored for patients enrolled in China's Family doctor contract service program, and rigorously tested its psychometric reliability, validity, along with conducting other pertinent assessments. However, the most substantial challenge in this endeavor was not merely the translation of the PCPCM into Chinese, but rather achieving a harmonized understanding of the core theories that underpin the functional features of primary care within the distinctive context of primary care system in mainland China. A key aspect was identifying the appropriate target population for the translated PCPCM and accurately determining which functional features, vital for health improvement, could be realistically experienced by this group. This foundational work is crucial for ensuring the effective and extensive future application of the PCPCM-SC-FDCP. This realization led us to thoroughly consider the relationship between the functional features of primary care, service modalities, and the adequate population in mainland China before starting the translation work. Theoretically, the performance and value of primary care are more likely dependent on the combined impact of its functional features rather than the clinical practice of individual diseases 1 . However, the current official metrics for evaluating primary care facilities and family doctor contract services in mainland China often emphasize process and outcome indicators for specific clinical and public health services 13,31,32 , thereby inadvertently downplaying the crucial role of primary care functions. Recent studies focusing on Chinese residents, especially those with chronic conditions, have revealed significant links between primary care's functional aspects — such as accessibility and continuity — and improved health status 33-35 . These findings underpin our translation and validation of the PCPCM for adequate primary care patients in Mainland China. However, it is essential that the patients' experiences with primary care functions, as evaluated by the translated PCPCM, should be universally and effectively acknowledged and understood within the context of China's actual primary care system at a population level. An important aspect to consider is the unique nature of China's healthcare system, which results in a diverse and sometimes ambiguous set of definitions for 'primary care patients,' particularly when compared to the understanding of the term in the US and Commonwealth countries. In mainland China, primary care is predominantly delivered by public facilities, such as urban community health service centers/stations and rural township health centers/village health stations 36 . Therefore, using the original English phrases of the PCPCM, such as 'my clinic' or 'my practice,' in surveys may lead Chinese residents and patients to mistakenly associate the evaluated healthcare services with private clinics, which are not the primary providers of primary care services. Furthermore, the term 'community residents,' theoretically potential patients at primary care facilities, may not be the most suitable respondents for survey research. In the absence of a gatekeeping mechanism, over 40% of these residents are typically infrequent users of these facilities and have limited engagement with genuine primary care services 11,38 . Consequently, targeting them for quality assessment of primary care could potentially yield biased data. Patients inclined to choose primary care facilities for their health needs represent a broader and more inclusive spectrum of primary care patients. However, it's crucial to recognize that the distinction in practice between doctors at primary care facilities and general hospitals in mainland China is not as pronounced as the difference between family physicians (general practitioners) and specialists in North America or Europe 11,23,39 . Additionally, the unique structure of the health system and the fluctuating nature of doctor-patient relationships may shape patients' perceptions of primary care's functional features. A notable example is the absence of a gate-keeping system and a stable referral mechanism, which could significantly lessen the perceived importance and effectiveness of the coordination function in these settings 11,39,40 . Given this reality, we made modifications to item 5 of the original PCPCM to better reflect the actual conditions. Ultimately, in the development of the PCPCM-SC-FDCP, we specifically targeted the patient population of primary care facilities who are enrolled in family doctor contracted services. This approach ensures that the selected population is likely to perceive distinct features such as continuity, partial coordination, and community orientation, which are integral to family doctor contracted services 13-15 . Another key advantage of the PCPCM-SC-FDCP, inheriting the original PCPCM's strengths, is its conciseness and the resultant lower cost of assessment. Performance measurement inherently adds a significant workload for primary care practitioners 16 . Currently, a considerable number of primary care practitioners in China are facing professional burnout, largely attributable to the extensive paperwork required for primary care and essential public health services 41,42 . Against this backdrop, reducing the extra costs associated with performance assessments is crucial for the widespread implementation and execution of quality evaluations in primary care. The PCPCM comprises only 11 specific questions, far fewer than other scales with similar purposes that have been translated into Chinese and applied in mainland China's primary care context, such as the full and simplified versions of the PCAT 43,44 , and General Practice Assessment Questionnaire (GPAQ) 45 . Practically, employing the PCPCM-SC-FDCP in health service research at primary care facilities or within community settings can greatly ease the workload for surveyors, who are often primary care practitioners themselves. It also allows for achieving better response rates, patient cooperation, and accuracy, thereby enhancing survey efficiency and feasibility. In our future endeavors, we aim to continuously update the wording and questions of the PCPCM-SC-FDCP, aligning them with the evolving landscape of China's primary care system. Given the vastness and diversity of mainland China, especially the differences in primary care systems between the eastern coastal provinces and the western provinces, as well as between urban and rural areas 46,47 , it is anticipated that the functional features of primary care in China may exhibit significant internal variations and local specificities. Therefore, it becomes imperative to conduct bottom-up research, exploring the existence, intensity, and trends of primary care functional features from the perspectives of different primary care patient groups. Such an approach is essential to fully understand and cater to the nuanced needs of primary care in diverse settings across the country. Conclusion Our study's findings indicate that the PCPCM-SC-FDCP is an effective patient-reported outcome measure, well-suited for assessing the experiences of primary care function among patients enrolled in family doctor contract services in mainland China. Looking to the future, this tool has significant potential for wider application across various provinces and within key demographic subgroups in mainland China. It offers an invaluable means to explore and quantify the performance and impact of functional features integral to the effectiveness of family doctor contract services within the primary care systems across the country. Declarations Ethics approval ： Ethical approval for this study was granted by the ethics review board at Peking University, assigned with the approval number: IRB00001052-23077. Consent to Participate declaration: not applicable Consent for publication: Not applicable. Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Additionally, the original datasets can be accessed on public data platforms: Competing interests: The authors declare that they have no competing interests Funding: This research was funded by the Shanghai Municipal Health Commission Health Policy Research Project (Grant No. 2023HP28&2023HP71), and by Shanghai Leading Talents Program (Grant No. YDH-20170627) Authors' contributions: Conceptualization, Y.W.; Methodology, Y.W. and H.J.; Data curation, Y.W.; Formal analysis, Y.W.; Funding acquisition, H.J. and D.Y.; Project administration, H.J. and D.Y.; Resources, Y.W. and H.J.; Supervision, H.J. and D.Y.; Validation, Y.W.; Writing—original draft, Y.W.; Writing—review and editing, Y.W., H.J. and D.Y. All authors have read and agreed to the published version of the manuscript. 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Global Health Res Policy. 2023;8(1):24. Xu W, Pan Z, Li Z, Lu S, Zhang L. Job burnout among primary healthcare workers in rural China: a multilevel analysis. Int J Environ Res Public Health. 2020;17(3):727. Lu S, Zhang L, Klazinga N, Kringos D. More public health service providers are experiencing job burnout than clinical care providers in primary care facilities in China. Hum Resour health. 2020;18(1):1–1. Yang H, Shi L, Lebrun LA, Zhou X, Liu J, Wang H. Development of the Chinese primary care assessment tool: data quality and measurement properties. Int J Qual Health Care. 2013;25(1):92–105. Mei J, Liang Y, Shi L, Zhao J, Wang Y, Kuang L. The development and validation of a rapid assessment tool of primary care in China. BioMed research international. 2016;2016. Zhang S, Zhang Y, Du L, Du J, Hu Y, Gu K, Xu J, Wang L, Zhang B, Lu X. Application and evaluation of general practice assessment questionnaire Chinese version for patients' satisfaction in community health-care service. Chin J Gen Practitioners. 2011:463–7. Tang S, Meng Q, Chen L, Bekedam H, Evans T, Whitehead M. Tackling the challenges to health equity in China. Lancet. 2008;372(9648):1493–501. Hu R, Liao Y, Du Z, Hao Y, Liang H, Shi L. Types of health care facilities and the quality of primary care: a study of characteristics and experiences of Chinese patients in Guangdong Province, China. BMC Health Serv Res. Dec; 2016;16(1):1–1. Tables Table 1. Methodology for Psychometric Analysis of PCPCM-SC-FDCP Psychometric analysis Methodology Criteria for Judgment Internal consistency reliability Utilizing Cronbach's Alpha to test the consistency among 11 items of PCPCM-SC-FDCP. Consistency ≥ 0.7 Stability reliability Two weeks following the initial survey, a follow-up survey employing the PCPCM-SC-FDCP was administered through telephone interviews with patients who had participated in the initial survey. During this process, the ICC was utilized to assess the consistency between the scores obtained from the first and the follow-up interviews. ICC≥0.5 Homogeneity An analysis was conducted to determine if each item of the PCPCM-SC-FDCP correlated with the scale's total score, excluding the item itself. This method, known as Item-Total Correlation, was applied consistently across all 11 items of the scale. Correlation≥0.3 Construct-related validity During the survey, data were collected for both the PCPCM-SC-FDCP and the Simplified Chinese version of PCAT. The relationship between these two sets of scores was examined through Spearman correlation. Correlation≥0.3 Criterion-related validity The survey collected data on patients' self-reported satisfaction levels using a five-point scale, which ranged from 'very satisfied' to 'very dissatisfied'. Subsequently, this satisfaction data was analyzed in relation to the scores of the PCPCM-SC-FDCP through Spearman correlation. Correlation≥0.3 Dimensionality Principal Component Analysis was conducted on the PCPCM-SC-FDCP to identify the underlying structure of the scale. A primary principal component was identified, characterized by a substantially higher proportion of variance in comparison to other components. This was further underscored by its leading eigenvalue, which was significantly larger than the eigenvalues of the remaining components. Model fit Rasch analysis was conducted to evaluate the fit of the PCPCM-SC-FDCP. This involves examining both the outfit MnSq and infit MnSq for each item. Outfit MnSq and infit MnSq values ranging between 0.7 and 1.4. ICC: Intraclass Correlation Coefficient PCPCM-SC-FDCP: Person-Centered Primary Care Measure-Simplified Chinese Version for Family Doctor Contracted Patients. PCAT: Primary Care Assessment Tool MnSq: Mean Square Table 2 The sociodemographic characteristics of surveyed primary care patients enrolled in family doctor contract services in mainland China All patients (n=583) Diagnosed with hypertension and diabetes (n=466) Aged 65 and older (n=411) Gender （ % ） Male 218(37.39) 182(39.06) 160(38.93) Female 365(62.61) 284(60.94) 251(61.07) Age （ Mean, Years ） 67.57 69.9 73.42 Education （ % ） Did Not Complete Primary School 16(2.74) 14(3.00) 15(3.65) Completed Primary School 93(15.95) 79(16.95) 82(19.95) Completed Junior Middle School 192(32.93) 159(34.12) 142(34.55) Completed High School 165(28.30) 140(30.04) 118(28.71) Completed College or Higher 117(20.07) 74(15.88) 54(13.14) Annual Household Income (Mean, Yuan ） 64127.05 60501.72 55797.15 EQ-5D Utility Index (Mean, Scores) 0.93 0.92 0.92 Duration of family doctor contracts (%) Less than 1 year 45(7.72) 45(7.72) 19(4.62) 1-2 years 34(5.83) 34(5.83) 23(5.60) 2-3 years 99(16.68) 99(16.98) 68(16.55) More than 3 years 405(69.47) 405(69.47) 301(73.24) Frequency of consultations with a family doctor in the past year (%) 1-2 Times 128 (21.96) 79(16.95) 69(16.79) 3-5 Times 131 (22.47) 101(21.67) 80(19.46) 6-10 Times 162 (27.79) 135(28.97) 120(29.20) More than 10 Times 162 (27.79) 151(32.40) 142(34.55) Table 3. Patient experience of surveyed primary care patients enrolled in family doctor contract services in mainland China All patients(n=583) Diagnosed with hypertension and diabetes(n=466) Aged 65 and older(n=411) PCPCM-SC-FDCP - Initial survey (Mean, Score) Item 1 3.83 3.80 3.80 Item 2 3.79 3.77 3.78 Item 3 3.68 3.67 3.65 Item 4 3.61 3.61 3.59 Item 5 3.68 3.67 3.68 Item 6 3.45 3.44 3.43 Item 7 3.67 3.65 3.64 Item 8 3.64 3.63 3.61 Item 9 3.58 3.58 3.56 Item 10 3.73 3.70 3.69 Item 11 3.70 3.68 3.69 Total 3.67 3.65 3.65 PCPCM-SC-FDCP - Follow-up survey (Mean, Score) Item 1 3.84 3.82 3.82 Item 2 3.73 3.70 3.73 Item 3 3.61 3.60 3.61 Item 4 3.57 3.57 3.58 Item 5 3.57 3.56 3.57 Item 6 3.32 3.32 3.32 Item 7 3.59 3.58 3.60 Item 8 3.58 3.59 3.60 Item 9 3.58 3.58 3.61 Item 10 3.67 3.68 3.69 Item 11 3.66 3.67 3.68 Total 3.61 3.61 3.62 Self-reported patient satisfaction with family doctor contracting services (%) Very Satisfied 440(75.47) 351(75.32) 310(75.43) Somewhat Satisfied 132(22.64) 104(22.32) 92(22.38) Neutral 11(1.89) 11(2.36) 9(2.19) PCAT (Mean, Score) First contact Utilization 3.74 3.72 3.70 First contact access 3.31 3.30 3.30 Ongoing care 3.53 3.51 3.50 Coordination 3.68 3.67 3.66 Coordination (information systems) 3.71 3.70 3.67 Comprehensiveness 3.47 3.46 3.42 Family-centeredness 3.75 3.74 3.72 Community orientation 3.69 3.68 3.65 Culturally competent 3.44 3.43 3.39 Total 3.59 3.57 3.55 PCPCM-SC-FDCP: Person-Centered Primary Care Measure-Simplified Chinese Version for Family Doctor Contracted Patients. PCAT: Primary Care Assessment Tool Table 4. Results of psychometric analyses of PCPCM-SC-FDCP Psychometric analysis All patients(n=583) Diagnosed with hypertension and/or diabetes(n=466) Aged 65 and older(n=411) Internal consistency reliability(Cronbach's α ) 0.94 0.94 0.95 Stability reliability (ICC) 0.56 0.55 0.58 Homogeneity (Spearman's Rank-Order Correlation) Item1 0.69* 0.73* 0.73* Item2 0.71* 0.75* 0.73* Item3 0.77* 0.81* 0.79* Item4 0.68* 0.70* 0.67* Item5 0.79* 0.82* 0.81* Item6 0.67* 0.67* 0.70* Item7 0.85* 0.87* 0.86* Item8 0.84* 0.84* 0.84* Item9 0.78* 0.79* 0.79* Item10 0.78* 0.81* 0.78* Item11 0.78* 0.81* 0.79* Construct-related validity (Spearman's Rank-Order Correlation) 0.72* 0.74* 0.77* Criterion-related validity (Spearman's Rank-Order Correlation) 0.54* 0.55* 0.56* Dimensionality Proportion of Variance Explained by Principal Component 1 0.65 0.68 0.66 Proportion of Variance Explained by other Components 0.01-0.08 0.01-0.07 0.01-0.07 Eigenvalue of Principal Component 1 7.17 7.52 7.36 Eigenvalues of Other Components 0.16-0.90 0.12-0.80 0.14-0.82 Model fit (Rasch Analysis ） 0.01 Infit Mean Square (Person) 0.99 0.97 0.98 Outfit Mean Square (Person) 1.04 1 1.03 Infit Mean Square (Item) 0.96 0.96 0.96 Outfit Mean Square (Item) 1.04 1 1.03 *:P ＜ 0.001 ICC: Intraclass Correlation Coefficient Additional Declarations No competing interests reported. Supplementary Files Supplementalfile.docx Cite Share Download PDF Status: Published Journal Publication published 31 Mar, 2025 Read the published version in BMC Primary Care → Version 1 posted Editorial decision: Revision requested 02 Apr, 2024 Submission checks completed at journal 29 Mar, 2024 Editor assigned by journal 29 Mar, 2024 First submitted to journal 18 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4120806","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286482200,"identity":"86b0d86f-64cc-4e27-83af-0b6f11a41460","order_by":0,"name":"Yang Wang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Wang","suffix":""},{"id":286482201,"identity":"8596477d-c8da-4d8b-be5e-24bb946a2f95","order_by":1,"name":"Dehua Yu","email":"","orcid":"","institution":"Yangpu Hospital, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Dehua","middleName":"","lastName":"Yu","suffix":""},{"id":286482202,"identity":"05dd8378-a8c1-41f7-ace2-40ddc067c420","order_by":2,"name":"Hua Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDCCAwwJBxgbGBj4wTweCSCRQKQWyTYStDAwgLQYHIMLEdDCd/vAw8OFOw7LG99vPvbwi4wFAz97jgHDzx24tUieS0g4PPPMYcNtx9jSjWWADpPseWPA2HsGtxaDMwwJh3nbDjNuO8ZjJi0B1GJwI8eAmbGNsBb7zW1QLfbEakncwMZjJvkBZIsEAS2SEC3pyTOOpaVJAwOZR+LMs4KDvXi08J3hSf7M22Zt2998+Jjkz546Of725I0PfuLRAoy9BDiTmbeHgQfEOIBPAwMDO0Ke8ccP/GpHwSgYBaNgZAIAoMtRB+EWKgAAAAAASUVORK5CYII=","orcid":"","institution":"Yangpu Hospital, Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Hua","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2024-03-18 07:20:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4120806/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4120806/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12875-025-02796-z","type":"published","date":"2025-03-31T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54106578,"identity":"f9e12347-dd70-4c9a-8e35-afa7150fa00d","added_by":"auto","created_at":"2024-04-04 17:22:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":220957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScree Plot: Principal Component Analysis of the PCPCM-SC-FDCP\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4120806/v1/04640f7dd3fa28dd8d221fe8.jpeg"},{"id":80083008,"identity":"bb1d088f-96b6-4aa9-9e09-b57e208afb64","added_by":"auto","created_at":"2025-04-07 16:09:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2149383,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4120806/v1/aef90837-9922-41c7-bf9a-14d55026881b.pdf"},{"id":54106580,"identity":"9b74403b-4d12-44d5-a413-00f365ece10c","added_by":"auto","created_at":"2024-04-04 17:22:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21964,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4120806/v1/e266edc247e8295bba51f16b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Translation, Adaptation, and Validation of Person-Centered Primary Care Measures for Patients in Family Doctor Contract Services within Mainland China","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimary care is pivotal in achieving enhanced outcomes in both population health and health equity, while simultaneously ensuring cost-effectiveness, as opposed to a specialist-centric healthcare\u003csup\u003e1,2\u003c/sup\u003e. Its efficacy is inherently tied to its distinctive functional features within healthcare services, such as accessibility, continuity, comprehensiveness, and coordination\u003csup\u003e2,3\u003c/sup\u003e. This highlights the critical importance of effective measuring these features to evaluate their performance and quality effectively\u003csup\u003e4\u003c/sup\u003e. To achieve this, various instruments have been developed and empirically validated in different countries. Notable among these are the Person-Centered Primary Care Measure (PCPCM), the Primary Care Assessment Tool (PCAT), and the Quality and Outcomes Framework (QOF) \u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA major challenge in assessing the functional features of primary care in Mainland China is its unique historical and societal context, intricately linked with a healthcare system undergoing reform. Established between the 1950s and 1970s, China's primary care initially aimed at providing accessible health services to rural and economically disadvantaged populations\u003csup\u003e6\u003c/sup\u003e. However, this focus shifted during the revenue-driven healthcare reforms of the 1970-1980s, which transitioned towards a disease-centric model of care\u003csup\u003e7\u003c/sup\u003e. Recently, China's primary care framework has evolved into a model significantly influenced by government-led planning and the structuring of healthcare institutions: despite an extensive network of nearly one million primary care facilities, which ensures the delivery of accessible health services to marginalized communities, the system's practice model and scope are less defined, especially when compared to the specialist outpatient clinics of general hospitals\u003csup\u003e8,9\u003c/sup\u003e. Moreover, the scope and quality of primary care are further compromised by a significant shortage of experienced practitioners, limited medical equipment, and a constrained range of pharmaceutical options\u003csup\u003e10\u003c/sup\u003e. Additionally, the lack of a primary care-led gatekeeping mechanism leads to a competitive landscape where secondary care providers vie with primary care facilities in a free market for routine clinical care. This competition poses significant challenges in establishing effective collaborative relationships\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOver the past decade, health system reforms in China have positioned the family doctor contract service program as a key policy initiative\u003csup\u003e12\u003c/sup\u003e. The core of this program is the formation of family doctor teams, which are essential for establishing long-term therapeutic relationships with local community residents who voluntarily contract with them. Each team can serve up to 2000 individuals and is centered around primary care facilities. These teams consist of primary care physicians, including general practitioners and community specialist doctors, and are supported by nurses, physician assistants, and public health doctors\u003csup\u003e13\u003c/sup\u003e. They provide a comprehensive array of primary care services to their contracted residents, including health consultations, routine physical examinations, diagnosis and treatment of common ailments, chronic disease management, and traditional Chinese medicine services\u003csup\u003e13\u003c/sup\u003e. Additionally, they assume responsibility for essential public health services in specific communities or villages, such as vaccinations, surveillance of certain infectious and non-communicable diseases, and preventive care for vulnerable populations like children, pregnant women, and the elderly\u003csup\u003e14\u003c/sup\u003e. In some regions, their clinical capacity has been enhanced through increased medical resources, such as expanded prescription capabilities and exclusive fast-track appointment slots, facilitating more efficient patient referrals to major hospitals\u003csup\u003e13\u003c/sup\u003e. With the government's goal to expand this program to at least 75% of China's population by 2035\u003csup\u003e15\u003c/sup\u003e, it demonstrates the potential to become a cornerstone of national primary care services with higher performance in mainland China in the future.\u003c/p\u003e \u003cp\u003eIn the evaluation of the functional features of China's primary care, particularly following the integration of the Family doctor contract service program, it becomes essential to use an assessment tool that is firmly rooted in the doctor-patient therapeutic relationship and remain cost-effective\u003csup\u003e16\u003c/sup\u003e. Moreover, this tool must be adaptable to the unique primary care environment of mainland China and capable of capturing patients' genuine perceptions of their beneficial experiences with primary care services\u003csup\u003e16\u003c/sup\u003e. We selected the PCPCM for its unparalleled ability to surpass other similar instruments: its eleven items succinctly capture the essential beneficial aspects of primary care, reflecting the direct experiences of the local patient population\u003csup\u003e17\u003c/sup\u003e. This framework allows for a more adaptable evaluation of varied patient responses to primary care across different social contexts and healthcare systems, unlike methods that gather responses from specific populations to fixed scenarios within a particular healthcare setting. Although the PCPCM has been translated and validated in various languages across 35 OECD countries and regions\u003csup\u003e18\u003c/sup\u003e, including Hong Kong\u003csup\u003e19,20\u003c/sup\u003e, China, the significant linguistic differences between traditional and simplified Chinese, along with the considerable disparities in the health systems of Hong Kong and mainland China, highlight the necessity for a version of the PCPCM specifically tailored for primary care patients who have enrolled in family doctor contract services in mainland China. Therefore, our study aims to translate, adapt, and validate the PCPCM in simplified Chinese to measure the patient experience within this population accurately.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThe original PCPCM consists of a question followed by 11 items that cover a spectrum of primary care functional features. Respondents are prompted to answer using a four-point Likert scale, with options ranging from \u0026quot;Definitely\u0026quot; to \u0026quot;Not at all\u0026rdquo;\u003csup\u003e17\u003c/sup\u003e. In this study, we adhered to the cross-cultural translation, adaptation, and validation guidelines proposed by Sousa and Rojjanasrirat\u003csup\u003e24\u003c/sup\u003e, employing a three-step process to develop a Simplified Chinese version of the PCPCM for patients under the family doctor contract service program (PCPCM-SC-FDCP).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of pre-final version of PCPCM-SC-FDCP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial phase in developing the PCPCM-SC-FDCP was grounded in a meticulous and culturally sensitive translation process. This phase involved two bilingual translators, both possessing fluency in English and Simplified Chinese. Their expertise extended beyond language proficiency; the first translator brought practical experience in general practice and public health, while the second translator was deeply familiar with the local primary care contexts and nuances of the family doctor contract service, although with a more limited background in health sciences. To ensure translation fidelity, these translators independently converted the PCPCM from English to Simplified Chinese. Special attention was given to aligning the translation with the specific needs and experiences of our target population\u0026mdash;patients under family doctor contracts. Their independent translations were then scrutinized by a third bilingual translator, an active general practitioner in a Chinese primary care setting. This translator\u0026apos;s role was pivotal in identifying and resolving inconsistencies between the two translations, focusing on terminology, sentence structure, conveyed meaning, and the suitability of the adaptations made. Through collaborative efforts, these translations were harmonized, culminating in a preliminary version of the PCPCM-SC-FDCP.\u003c/p\u003e\n\u003cp\u003eFurther refining the translation, two additional bilingual translators, both native English speakers studying in China, independently back-translated the preliminary version into English. One translator specialized in general practice, while the other, lacking a specialized background in health sciences, brought a different perspective. A comprehensive review session with all five translators was then organized. The insights gained from this session were instrumental in refining and finalizing the translation, culminating in the development of the pre-final version of the PCPCM-SC-FDCP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePilot Testing\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of PCPCM-SC-FDCP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pilot testing of the pre-final versions of the PCPCM-SC-FDCP was conducted in two distinct phases. In the initial phase, the PCPCM-SC-FDCP was administered to a group of 20 primary care patients who were under contract with family doctors. These patients were reached through general practitioners in five provinces (Beijing, Chongqing, Henan, Liaoning, and Shanghai). The survey was conducted using an online questionnaire. They were requested to evaluate the clarity of the instructions, items, and response format of the translated scale using a straightforward dichotomous scale (clear or unclear). The advancement to the next phase of testing was contingent on achieving clarity as reported by over 80% of the participants, encompassing the instructions, response format, and individual items.\u003c/p\u003e\n\u003cp\u003eAdditionally, we assembled a panel of 10 experts from mainland China, all well-versed in the functional features of primary care. These experts were selected from general practice and public health departments within both practice and research institutes. Following their feedback, we made meticulous adjustments to the wording, proceeding only after more than 80% of the panel agreed that the instructions, response format, and each item were sufficiently clear to confirm conceptual equivalence.\u003c/p\u003e\n\u003cp\u003eDuring the second phase, we engaged these ten experts to evaluate the content relevance of each item using a four-point scale, where 1=not relevant, 2=unable to assess relevance, 3=relevant but requiring minor alteration, and 4=very relevant and succinct. Items receiving a rating of 1 (not relevant) or 2 (unable to assess relevance) were further refined. To ensure robust content validity, we set a minimum threshold of Scale-Content Validity Index/Average (S-CVI/Ave) \u0026ge; 0.90\u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePsychometric test\u003c/strong\u003e\u003cstrong\u003eing of PCPCM-SC-FDCP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Shanghai, China, we successfully recruited a total of 583 primary care patients under family doctor contract service program from 10 primary care facilities. These institutions were strategically selected across urban (4 facilities), suburban (3 facilities), and rural (3 facilities) settings to ensure diverse representation. Each institution enrolled between 50 to 100 patients. In accordance with established empirical standards, this sample size was considered adequate for the psychometric analysis of the eleven dimensions of the pre-final versions of the PCPCM-SC-FDCP\u003csup\u003e24\u003c/sup\u003e. The inclusion criteria included: (1) being aged 18 years or older; (2) cognitive alertness and ability to read or understand the relevant informed consent documents; (3) having utilized at least one primary care service from a family doctor in the past 12 months,\u0026nbsp;consistent with the implementation recommendations for the target population set forth by the American Academy of Family Physicians\u003csup\u003e\u0026nbsp;25\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAs indicated in Table 1, we collected data intended to support psychometric analyses of the PCPCM-SC-FDCP. Additionally, we gathered data on participants\u0026apos; gender, age, educational level, mean annually income, EQ-5D utility scores, duration of family doctor contracts, and frequency of consultations with a family doctor in the past year. Using this data, we analyzed the internal consistency reliability, stability reliability, homogeneity, construct-related validity, criterion-related validity, dimensionality, and model fit of the PCPCM-SC-FDCP. We established adequate standards for these analyses based on relevant methodological guidelines\u003csup\u003e26-30\u003c/sup\u003e. Moreover, we conducted two subgroup analyses as sensitivity analysis, specifically focusing on individuals with diabetes and/or hypertension, and seniors aged 65 and above. These groups represent the primary and largest demographics for Family doctor contract service program in mainland China\u003csup\u003e15\u003c/sup\u003e. The statistical analyses were carried out using several software tools, including Stata 17.0 SE (StataCorp LLC, College Station, TX, USA), IBM SPSS Statistics V.26.0 (IBM Corp., Armonk, NY, USA), R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria), and Winsteps 3.72.3 (Winsteps.com, Beaverton, OR, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was granted by the ethics review board at Peking University, with the approval number IRB00001052-23077. In accordance with the permissions granted by the ethics committee, informed consent was obtained verbally from all participants. The participants were fully informed about the purpose of the study, their right to withdraw at any time without any consequences, and the measures taken to ensure their privacy and the confidentiality of their responses. Verbal consent was deemed appropriate by the ethics committee due to the nature of the study and was documented accordingly.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThrough the dedicated efforts of five translators, the PCPCM was effectively translated into Chinese (see Table S1). Feedback from 20 primary care patients under family doctor contract service program and 10 experts led to several revisions in the PCPCM-SC-FDCP. The most significant change was to item 5, concerning primary care\u0026apos;s coordination function. Originally, it read \u0026apos;My family doctor or the team could coordinate the care I get from multiple places.\u0026apos; This was revised to \u0026apos;My family doctor or the team is aware of the medical services I receive from various institutions,\u0026apos; following insights from many experts (70%) and patients (50%). They noted that due to the lack of a robust gate-keeping system and formal referral mechanism in mainland China, primary care patients often lack experience with care coordination, making it challenging to evaluate their family doctor teams\u0026rsquo; coordination capabilities (see Table S2).\u003c/p\u003e\n\u003cp\u003eAfter revising the questionnaire and confirming that over 80% of evaluators found its content clear, we calculated theS-CVI/Ave for each PCPCM-SC-FDCP item based on 10 experts\u0026apos; responses. The S-CVI/Ave was 0.90, enabling us to move forward with further psychometric analysis (see Table S2).\u003c/p\u003e\n\u003cp\u003eOur survey of 583 patients showed that 466 (79.93%) had hypertension or diabetes, and 411 (70.49%) were aged 65 or older. Sociodemographic characteristics are detailed in Table 2, including gender, age, education level, average annual income, and EQ-5D utility scores. Regarding family doctor services, most patients had long-term contracts and frequent visits within the last year.\u003c/p\u003e\n\u003cp\u003eTable 3 presents patients\u0026apos; reported experiences with primary care. Survey results revealed very positive experiences among patients with family doctor contracts, indicated by the overall mean scores of the PCPCM-SC-FDCP and the final scores of the full version of the PCAT, as well as the high satisfaction rate with family doctor contract services.\u003c/p\u003e\n\u003cp\u003eThe PCPCM-SC-FDCP\u0026apos;s psychometric performance, detailed in Table 4 and Figure 1, was excellent. Noteworthy were its internal consistency reliability (Cronbach\u0026apos;s \u0026alpha;=0.94), homogeneity (Correlation=0.67-0.85), construct-related validity (Correlation=0.72, p\u0026le;0.01), and criterion-related validity (Correlation=0.54, p\u0026le;0.01). Rasch analysis confirmed the model fit, with Outfit Mean Square and Infit Mean Square values close to 1. The PCPCM-SC-FDCP also exhibited unidimensionality, aligning its 11 items with the same concept. Stability reliability (ICC=0.56) was slightly below ideal but still acceptable.\u003c/p\u003e\n\u003cp\u003eTargeted psychometric analyses on the two subgroups also demonstrated the PCPCM-SC-FDCP\u0026apos;s good psychometric properties for these populations (see Table 4 and Figure 1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough this study, we translated the PCPCM into a Simplified Chinese version, specifically tailored for patients enrolled in China\u0026apos;s Family doctor contract service program, and rigorously tested its psychometric reliability, validity, along with conducting other pertinent assessments. However, the most substantial challenge in this endeavor was not merely the translation of the PCPCM into Chinese, but rather achieving a harmonized understanding of the core theories that underpin the functional features of primary care within the distinctive context of primary care system in mainland China. A key aspect was identifying the appropriate target population for the translated PCPCM and accurately determining which functional features, vital for health improvement, could be realistically experienced by this group. This foundational work is crucial for ensuring the effective and extensive future application of the PCPCM-SC-FDCP.\u003c/p\u003e\n\u003cp\u003eThis realization led us to thoroughly consider the relationship between the functional features of primary care, service modalities, and the adequate population in mainland China before starting the translation work. Theoretically, the performance and value of primary care are more likely dependent on the combined impact of its functional features rather than the clinical practice of individual diseases\u003csup\u003e1\u003c/sup\u003e. However, the current official metrics for evaluating primary care facilities and family doctor contract services in mainland China often emphasize process and outcome indicators for specific clinical and public health services\u003csup\u003e13,31,32\u003c/sup\u003e,\u0026nbsp;thereby inadvertently downplaying the crucial role of primary care functions. Recent studies focusing on Chinese residents, especially those with chronic conditions, have revealed significant links between primary care\u0026apos;s functional aspects \u0026mdash; such as accessibility and continuity \u0026mdash; and improved health status\u003csup\u003e33-35\u003c/sup\u003e. These findings underpin our translation and validation of the PCPCM for adequate primary care patients in Mainland China. However, it is essential that the patients\u0026apos; experiences with primary care functions, as evaluated by the translated PCPCM, should be universally and effectively acknowledged and understood within the context of China\u0026apos;s actual primary care system at a population level.\u003c/p\u003e\n\u003cp\u003eAn important aspect to consider is the unique nature of China\u0026apos;s healthcare system, which results in a diverse and sometimes ambiguous set of definitions for \u0026apos;primary care patients,\u0026apos; particularly when compared to the understanding of the term in the US and Commonwealth countries. In mainland China, primary care is predominantly delivered by public facilities, such as urban community health service centers/stations and rural township health centers/village health stations\u003csup\u003e36\u003c/sup\u003e. Therefore, using the original English phrases of the PCPCM, such as \u0026apos;my clinic\u0026apos; or \u0026apos;my practice,\u0026apos; in surveys may lead Chinese residents and patients to mistakenly associate the evaluated healthcare services with private clinics, which are not the primary providers of primary care services. Furthermore, the term \u0026apos;community residents,\u0026apos; theoretically potential patients at primary care facilities, may not be the most suitable respondents for survey research. In the absence of a gatekeeping mechanism, over 40% of these residents are typically infrequent users of these facilities and have limited engagement with genuine primary care services\u003csup\u003e11,38\u003c/sup\u003e. Consequently, targeting them for quality assessment of primary care could potentially yield biased data.\u003c/p\u003e\n\u003cp\u003ePatients inclined to choose primary care facilities for their health needs represent a broader and more inclusive spectrum of primary care patients. However, it\u0026apos;s crucial to recognize that the distinction in practice between doctors at primary care facilities and general hospitals in mainland China is not as pronounced as the difference between family physicians (general practitioners) and specialists in North America or Europe\u003csup\u003e11,23,39\u003c/sup\u003e. Additionally, the unique structure of the health system and the fluctuating nature of doctor-patient relationships may shape patients\u0026apos; perceptions of primary care\u0026apos;s functional features. A notable example is the absence of a gate-keeping system and a stable referral mechanism, which could significantly lessen the perceived importance and effectiveness of the coordination function in these settings\u003csup\u003e11,39,40\u003c/sup\u003e. Given this reality, we made modifications to item 5 of the original PCPCM to better reflect the actual conditions. Ultimately, in the development of the PCPCM-SC-FDCP, we specifically targeted the patient population of primary care facilities who are enrolled in family doctor contracted services. This approach ensures that the selected population is likely to perceive distinct features such as continuity, partial coordination, and community orientation, which are integral to family doctor contracted services\u003csup\u003e13-15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAnother key advantage of the PCPCM-SC-FDCP, inheriting the original PCPCM\u0026apos;s strengths, is its conciseness and the resultant lower cost of assessment. Performance measurement inherently adds a significant workload for primary care practitioners\u003csup\u003e16\u003c/sup\u003e. Currently, a considerable number of primary care practitioners in China are facing professional burnout, largely attributable to the extensive paperwork required for primary care and essential public health services\u003csup\u003e41,42\u003c/sup\u003e. Against this backdrop, reducing the extra costs associated with performance assessments is crucial for the widespread implementation and execution of quality evaluations in primary care. The PCPCM comprises only 11 specific questions, far fewer than other scales with similar purposes that have been translated into Chinese and applied in mainland China\u0026apos;s primary care context, such as the full and simplified versions of the PCAT\u003csup\u003e43,44\u003c/sup\u003e, and General Practice Assessment Questionnaire (GPAQ)\u003csup\u003e45\u003c/sup\u003e. Practically, employing the PCPCM-SC-FDCP in health service research at primary care facilities or within community settings can greatly ease the workload for surveyors, who are often primary care practitioners themselves. It also allows for achieving better response rates, patient cooperation, and accuracy, thereby enhancing survey efficiency and feasibility.\u003c/p\u003e\n\u003cp\u003eIn our future endeavors, we aim to continuously update the wording and questions of the PCPCM-SC-FDCP, aligning them with the evolving landscape of China\u0026apos;s primary care system. Given the vastness and diversity of mainland China, especially the differences in primary care systems between the eastern coastal provinces and the western provinces, as well as between urban and rural areas\u003csup\u003e46,47\u003c/sup\u003e, it is anticipated that the functional features of primary care in China may exhibit significant internal variations and local specificities. Therefore, it becomes imperative to conduct bottom-up research, exploring the existence, intensity, and trends of primary care functional features from the perspectives of different primary care patient groups. Such an approach is essential to fully understand and cater to the nuanced needs of primary care in diverse settings across the country.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study\u0026apos;s findings indicate that the PCPCM-SC-FDCP is an effective patient-reported outcome measure, well-suited for assessing the experiences of primary care function among patients enrolled in family doctor contract services in mainland China. Looking to the future, this tool has significant potential for wider application across various provinces and within key demographic subgroups in mainland China. It offers an invaluable means to explore and quantify the performance and impact of functional features integral to the effectiveness of family doctor contract services within the primary care systems across the country.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eEthical approval for this study was granted by the ethics review board at Peking University, assigned with the approval number: IRB00001052-23077.\u003c/p\u003e\n\u003cp\u003eConsent to Participate declaration: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Additionally, the original datasets can be accessed on public data platforms:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was funded by the Shanghai Municipal Health Commission Health Policy Research Project (Grant No. 2023HP28\u0026amp;2023HP71), and by Shanghai Leading Talents Program (Grant No. YDH-20170627)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, Y.W.; Methodology, Y.W. and H.J.; Data curation, Y.W.; Formal analysis, Y.W.; Funding acquisition, H.J. and D.Y.; Project administration, H.J. and D.Y.; Resources, Y.W. and H.J.; Supervision, H.J. and D.Y.; Validation, Y.W.; Writing\u0026mdash;original draft, Y.W.; Writing\u0026mdash;review and editing, Y.W., H.J. and D.Y. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe are immensely grateful to all the Shanghai general practitioners and patients who assisted us in testing the PCPCM-SC-FDCP. Additionally, our heartfelt thanks go to Professor Kurt Stange and fellow researchers at the Larry A. Green Center for granting us permission to translate the PCPCM and utilize it to evaluate the functional features of primary care for patients enrolled in the family doctor contract service program.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStange KC, Ferrer RL. The paradox of primary care. Annals Family Med. 2009;7(4):293\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStarfield B, Shi L, Macinko J. Contribution of primary care to health systems and health. 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The Primary Health Care and Health Promotion Law of the People's Republic of China [Internet]. 2019 [cited 2024 Jan 11]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gov.cn/xinwen/2019-12/29/content_5464861.htm\u003c/span\u003e\u003cspan address=\"http://www.gov.cn/xinwen/2019-12/29/content_5464861.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Birch S, Zhu W, Ma H, Embrett M, Meng Q. Coordination of care in the Chinese health care systems: a gap analysis of service delivery from a provider perspective. BMC Health Serv Res. 2016;16(1):1\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Zhang J, Lu J, Wei X. Patient views of the good doctor in primary care: a qualitative study in six provinces in China. 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The development and validation of a rapid assessment tool of primary care in China. BioMed research international. 2016;2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang S, Zhang Y, Du L, Du J, Hu Y, Gu K, Xu J, Wang L, Zhang B, Lu X. Application and evaluation of general practice assessment questionnaire Chinese version for patients' satisfaction in community health-care service. Chin J Gen Practitioners. 2011:463\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang S, Meng Q, Chen L, Bekedam H, Evans T, Whitehead M. Tackling the challenges to health equity in China. Lancet. 2008;372(9648):1493\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu R, Liao Y, Du Z, Hao Y, Liang H, Shi L. Types of health care facilities and the quality of primary care: a study of characteristics and experiences of Chinese patients in Guangdong Province, China. BMC Health Serv Res. Dec; 2016;16(1):1\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Methodology for Psychometric Analysis of PCPCM-SC-FDCP\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePsychometric analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.474747474747474%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCriteria for Judgment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal consistency reliability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.474747474747474%\" valign=\"top\"\u003e\n \u003cp\u003eUtilizing Cronbach\u0026apos;s Alpha to test the consistency among 11 items of PCPCM-SC-FDCP.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\" valign=\"top\"\u003e\n \u003cp\u003eConsistency \u0026ge; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStability reliability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.474747474747474%\" valign=\"top\"\u003e\n \u003cp\u003eTwo weeks following the initial survey, a follow-up survey employing the PCPCM-SC-FDCP was administered through telephone interviews with patients who had participated in the initial survey. During this process, the ICC was utilized to assess the consistency between the scores obtained from the first and the follow-up interviews.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\" valign=\"top\"\u003e\n \u003cp\u003eICC\u0026ge;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHomogeneity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.474747474747474%\" valign=\"top\"\u003e\n \u003cp\u003eAn analysis was conducted to determine if each item of the PCPCM-SC-FDCP correlated with the scale\u0026apos;s total score, excluding the item itself. This method, known as Item-Total Correlation, was applied consistently across all 11 items of the scale.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation\u0026ge;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstruct-related validity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.474747474747474%\" valign=\"top\"\u003e\n \u003cp\u003eDuring the survey, data were collected for both the PCPCM-SC-FDCP and the Simplified Chinese version of PCAT. The relationship between these two sets of scores was examined through Spearman correlation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation\u0026ge;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCriterion-related validity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.474747474747474%\" valign=\"top\"\u003e\n \u003cp\u003eThe survey collected data on patients\u0026apos; self-reported satisfaction levels using a five-point scale, which ranged from \u0026apos;very satisfied\u0026apos; to \u0026apos;very dissatisfied\u0026apos;. Subsequently, this satisfaction data was analyzed in relation to the scores of the PCPCM-SC-FDCP through Spearman correlation.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation\u0026ge;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDimensionality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.474747474747474%\" valign=\"top\"\u003e\n \u003cp\u003ePrincipal Component Analysis was conducted on the PCPCM-SC-FDCP to identify the underlying structure of the scale.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\" valign=\"top\"\u003e\n \u003cp\u003eA primary principal component was identified, characterized by a substantially higher proportion of variance in comparison to other components. This was further underscored by its leading eigenvalue, which was significantly larger than the eigenvalues of the remaining components.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel fit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.474747474747474%\" valign=\"top\"\u003e\n \u003cp\u003eRasch analysis was conducted to evaluate the fit of the PCPCM-SC-FDCP. This involves examining both the outfit MnSq and infit MnSq for each item.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\" valign=\"top\"\u003e\n \u003cp\u003eOutfit MnSq and infit MnSq values ranging between 0.7 and 1.4.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eICC: Intraclass Correlation Coefficient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCPCM-SC-FDCP: Person-Centered Primary Care Measure-Simplified Chinese Version for Family Doctor Contracted Patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCAT: Primary Care Assessment Tool\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMnSq: Mean Square\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 The sociodemographic characteristics of surveyed primary care patients enrolled in family doctor contract services in mainland China\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=583)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosed with hypertension and diabetes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=466)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAged 65 and older\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=411)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e218(37.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e182(39.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e160(38.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e365(62.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e284(60.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e251(61.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eMean, Years\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e67.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e69.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e73.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid Not Complete Primary School\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e16(2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e14(3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e15(3.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompleted Primary School\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e93(15.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e79(16.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e82(19.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompleted Junior Middle School\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e192(32.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e159(34.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e142(34.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompleted High School\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e165(28.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e140(30.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e118(28.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompleted College or Higher\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e117(20.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e74(15.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e54(13.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual Household Income (Mean, Yuan\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e64127.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e60501.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e55797.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEQ-5D Utility Index (Mean, Scores)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.74729241877257%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of family doctor contracts (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.3971119133574%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLess than 1 year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e45(7.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e45(7.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e19(4.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1-2 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e34(5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e34(5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e23(5.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-3 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e99(16.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e99(16.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e68(16.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMore than 3 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e405(69.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e405(69.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e301(73.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"80.14440433212997%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency of consultations with a family doctor in the past year (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1-2 Times\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e128 (21.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e79(16.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e69(16.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3-5 Times\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e131 (22.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e101(21.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e80(19.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6-10 Times\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e162 (27.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e135(28.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e120(29.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.24050632911393%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMore than 10 Times\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e162 (27.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.43399638336347%\" valign=\"top\"\u003e\n \u003cp\u003e151(32.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.89150090415913%\" valign=\"top\"\u003e\n \u003cp\u003e142(34.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Patient experience of surveyed primary care patients enrolled in family doctor contract services in mainland China\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients(n=583)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosed with hypertension and diabetes(n=466)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAged 65 and older(n=411)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.888086642599276%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCPCM-SC-FDCP -\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInitial survey (Mean, Score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.888086642599276%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCPCM-SC-FDCP -\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFollow-up survey (Mean, Score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem 11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"76.53429602888086%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-reported patient satisfaction with family doctor contracting services (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVery Satisfied\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e440(75.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e351(75.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e310(75.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSomewhat Satisfied\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e132(22.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e104(22.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e92(22.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutral\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e11(1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e11(2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e9(2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.888086642599276%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCAT (Mean, Score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirst contact Utilization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirst contact access\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOngoing care\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoordination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoordination\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(information systems)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComprehensiveness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily-centeredness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity orientation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCulturally competent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.74007220216607%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\" valign=\"top\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.465703971119133%\" valign=\"top\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePCPCM-SC-FDCP: Person-Centered Primary Care Measure-Simplified Chinese Version for Family Doctor Contracted Patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCAT: Primary Care Assessment Tool\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Results of psychometric analyses of PCPCM-SC-FDCP\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePsychometric analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients(n=583)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosed with hypertension and/or diabetes(n=466)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAged 65 and older(n=411)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal consistency reliability(Cronbach\u0026apos;s\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026alpha;\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStability reliability (ICC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHomogeneity (Spearman\u0026apos;s Rank-Order Correlation)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.69*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.73*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.73*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.71*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.75*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.73*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.77*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.81*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.79*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.68*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.70*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.67*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.79*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.82*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.81*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.67*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.67*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.70*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.85*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.87*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.86*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.84*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.84*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.84*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.78*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.79*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.79*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.78*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.81*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.78*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.78*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.81*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.79*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstruct-related validity (Spearman\u0026apos;s Rank-Order Correlation)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.72*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.74*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.77*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCriterion-related validity (Spearman\u0026apos;s Rank-Order Correlation)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.54*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.55*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.56*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDimensionality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion of Variance Explained by Principal Component 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion of Variance Explained by other Components\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.01-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.01-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.01-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEigenvalue of Principal Component 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEigenvalues of Other Components\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.16-0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.12-0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.14-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel fit (Rasch Analysis\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfit Mean Square (Person)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutfit Mean Square (Person)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfit Mean Square (Item)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.608247422680414%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutfit Mean Square (Item)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e*:P\u003c/strong\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICC:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIntraclass Correlation Coefficient\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Primary Care, Quality measurement, Quality improvement, Patient Reported Outcome Measure, China","lastPublishedDoi":"10.21203/rs.3.rs-4120806/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4120806/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eIn the context of China's health reforms enhancing its primary care function through Family Doctor Contract Service Program, effectively measuring its health-beneficial features is paramount. This study endeavors to translate, adapt, and validate the Person-Centered Primary Care Measure (PCPCM) for primary care patients enrolled in family doctor contract services in mainland China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e Following the guidelines by Sousa and Rojjanasrirat, we translated and adapted the PCPCM into simplified Chinese and evaluated its psychometric properties. Our assessment involved 583 patients in family doctor contract services from 10 primary care facilities in Shanghai, China. We analyzed various aspects, including internal consistency, stability, homogeneity, construct-related validity, criterion-related validity, dimensionality, and model fit of the adapted PCPCM. Additionally, we conducted subgroup analyses focusing on patients with hypertension and/or diabetes and seniors aged 65 and above.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe adaptation resulted in the PCPCM for patients under the family doctor contract service program(PCPCM-SC-FDCP), tailored for primary care patients under the family doctor contract service program in mainland China. Initial pilot testing led to refinements for clearer applicability, particularly for Item 5. The PCPCM-SC-FDCP demonstrated excellent internal consistency (Cronbach's α\u0026thinsp;=\u0026thinsp;0.94), homogeneity (Correlation\u0026thinsp;=\u0026thinsp;0.67\u0026ndash;0.85), construct-related validity (Correlation\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and criterion-related validity (Correlation\u0026thinsp;=\u0026thinsp;0.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alongside satisfactory dimensionality and model fit. Stability reliability (ICC\u0026thinsp;=\u0026thinsp;0.56), while slightly below the ideal, was deemed acceptable. The instrument also performed well in subgroup analyses.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThe PCPCM-SC-FDCP proves to be an effective patient-reported outcome measure, for measuring patient experiences with primary care's functional features among those enrolled in family doctor contract services in mainland China. Its widespread adoption is anticipated to significantly aid the strategic reform of China's primary care system by highlighting and improving functional features within the local healthcare framework.\u003c/p\u003e","manuscriptTitle":"Translation, Adaptation, and Validation of Person-Centered Primary Care Measures for Patients in Family Doctor Contract Services within Mainland China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-04 17:22:25","doi":"10.21203/rs.3.rs-4120806/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-02T07:58:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-29T23:44:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-29T23:44:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Primary Care","date":"2024-03-18T07:17:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b0625c76-e5d0-4cfd-b3ca-2ece7d45df81","owner":[],"postedDate":"April 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-07T16:08:59+00:00","versionOfRecord":{"articleIdentity":"rs-4120806","link":"https://doi.org/10.1186/s12875-025-02796-z","journal":{"identity":"bmc-primary-care","isVorOnly":false,"title":"BMC Primary Care"},"publishedOn":"2025-03-31 15:57:44","publishedOnDateReadable":"March 31st, 2025"},"versionCreatedAt":"2024-04-04 17:22:25","video":"","vorDoi":"10.1186/s12875-025-02796-z","vorDoiUrl":"https://doi.org/10.1186/s12875-025-02796-z","workflowStages":[]},"version":"v1","identity":"rs-4120806","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4120806","identity":"rs-4120806","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}