Determinants of willingness to use medical visit companion services among older adults in China: A Shapley value approach

Determinants of willingness to use medical visit companion services among older adults in China: A Shapley value approach | 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 Determinants of willingness to use medical visit companion services among older adults in China: A Shapley value approach Jiabin Xu, Jianming Wang, Linyi Zhu, Dandan Li, Yan Cai, Lulu Tang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7211539/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background The rapid digital transformation of healthcare, coupled with the increasing prevalence of empty-nest families in China, has widened disparities in healthcare access among older adults, especially in navigating technology-dependent services. Medical visit companion services (MVCS) may help address these challenges and reduce pressure on the healthcare system. However, as an emerging service in China, MVCS remain insufficiently studied regarding older adults' willingness to engage with them and the factors influencing this willingness. This study aimed to assess older adults’ willingness to use MVCS and to identify and quantify the determinants. Methods Drawing on Anderson’s health behavior model, we developed an analytical framework incorporating predisposing characteristics, enabling resources, and need factors to examine older adults’ willingness to use MVCS in China. Cross-sectional data from 494 participants in Zhejiang Province were analyzed using χ² tests, H tests, ordered logistic regression, and Shapley value decomposition to identify factors influencing MVCS willingness and to quantify their contributions. Results The mean score for willingness to use MVCS was 3.27 ± 1.23. Higher education level, MVCS awareness, monthly family income, individual accompaniment demand, and perceived need for assistance were positively associated with willingness. Older age, greater self-rated ease of medical visits, stronger social support, better self-rated health, and higher medical visit autonomy were negatively associated. Shapley value decomposition indicated that need factors (48.73%) were the primary drivers of willingness, with self-rated health (21.29%) as the most predictive. Enabling resources (26.90%) and predisposing characteristics (24.02%) also contributed substantially, together explaining most of the variance in willingness. Conclusions The willingness of Chinese older adults to engage with MVCS is influenced by predisposing characteristics, enabling resources, and need factors, with the need factors demonstrating the strongest explanatory power. To enhance older adults’ willingness to utilize MVCS and promote sustainable services development, improving health literacy may strengthen their capacity to recognize health problems and articulate their actual needs. Service providers should be equipped to identify older adults most likely to engage with MVCS. Tailoring services to meet diverse needs is essential to optimize MVCS effectiveness. Older adult Medical visit companion services utilization Anderson’s model Shapley value method Influencing factors Background According to the China Statistical Yearbook 2024 , individuals aged 60 and older account for 22.0% of the population, surpassing the World Health Organization (WHO) threshold defining an "aged society" [ 1 ] . This demographic shift has substantially increased the demand for long-term healthcare services. In China, there is a marked mismatch between the allocation of healthcare resources and regional needs. High-quality medical resources are disproportionately concentrated in tertiary hospitals and urban areas, whereas primary healthcare institutions and rural regions face severe shortages [ 2 ] . This imbalance has exacerbated challenges for older adults seeking high-quality care [ 3 ] . Digital healthcare services have played a pivotal role in addressing these challenges and supporting the goal of "medical care for the aged" [ 4 ] . However, the implementation of digital healthcare services faces numerous challenges. One of the most pressing issues is the digital divide affecting older adults. A study by Li et al. involving 155,695 participants found that internet use rates among adults aged 60 years and older were 5.56% in China, compared with 39.37% in Mexico, and 58.01% in the United States [ 5 ] . These findings indicate that older adults constitute a prototypical digitally vulnerable group within digital healthcare. They also frequently experience cognitive impairments and physical limitations when engaging with digital healthcare services [ 6 , 7 ] . This disparity directly limits older adults’ access to digital healthcare and exacerbates existing healthcare inequalities [ 8 , 9 ] . To overcome these challenges, older adults often rely on external assistance, particularly from adult children [ 10 ] . However, such support networks are frequently inadequate. Research based on the US Census and the University of Michigan Health and Retirement Study has shown that approximately 22% of adults aged 65 and older lack accessible kin, a legally designated surrogate, or a caregiver [ 11 ] . Similar patterns are observed in China [ 12 ] . These limited support networks are insufficient to meet older adults’ needs for medical companionship. Addressing the medical companionship needs of older adults is a global public health priority that requires collaboration across families, governments, and societies. Medical companionship originated from the US Patient Navigation (PN) model [ 13 ] , which is defined as an individualized intervention in cancer care aimed at addressing access barriers and facilitating timely healthcare utilization, diagnosis, and treatment. PN roles require specialized training for specific health issues, as oncology care coordination necessitates advanced clinical expertise [ 14 ] . The PN model has gradually been expanded to various chronic diseases (e.g., diabetes, HIV infection, cardiovascular disease, chronic kidney disease, and dementia) to improve patient outcomes [ 15 ] . This model has undergone significant local adaptation within the Chinese healthcare system. In China, medical companionship is referred to as medical visit companion services (MVCS). Against the backdrop of rapid population aging and imbalanced distribution of healthcare resources, MVCS are evolving in China to address the growing demand for socialized long-term care among older adults and to bridge gaps in healthcare access [ 16 ] . MVCS refer to assistance provided by trained non-professional caregivers to three main groups: older adults, pregnant women, and individuals with mobility impairments or limited capacity to access healthcare independently [ 17 ] . MVCS involve trained non-professional caregivers who provide non-clinical support, including practical assistance (scheduling appointments, navigating departments, retrieving medications, arranging hospital admissions), as well as psychosocial support to facilitate doctor-patient communication and offer emotional assistance [ 17 – 20 ] . This approach helps older adults better understand medical information and enhances the effectiveness of their medical visits. It also addresses the practical problem of limited access to medical care [ 21 ] . Nevertheless, MVCS remain in the early stages of development and face several challenges. In 2025, the Shanghai Civil Affairs Bureau launched the Pilot Program for Assisted Medical Visit Companion Services for Older Adults , aiming to enhance the professionalism and standardization of the service. The program outlines measures to standardize service protocols, develop specialized companion teams, and reinforce institutional safeguards [ 22 ] . These initiatives directly address the issues of service quality and operational efficiency, laying the groundwork for broader implementation. Existing studies have primarily employed qualitative methods to explore older adults' willingness to use MVCS and their specific service needs [ 16 , 23 – 25 ] . Only a limited number of studies have used quantitative methods to investigate older adults' willingness to engage with MVCS. However, these studies often lack a theoretical framework, which limits the rigor and generalizability of their findings [ 26 ] . Most quantitative studies have examined MVCS from the perspective of adult children. Although this approach facilitates data collection, the absence of older adults' perspectives introduces inherent bias into the findings [ 27 , 28 ] . These gaps hinder the in-depth exploration of factors influencing older adults' willingness to use MVCS and prevent medical companions from identifying those most likely to engage with such services. Consequently, resource allocation remains inefficient. Moreover, no previous study has examined older adults’ specific content preferences for MVCS, limiting the development of tailored service designs. Additionally, most researchers have investigated influencing factors directly, without categorizing them, assessing their relative importance, or grounding their studies in theoretical frameworks [ 25 , 27 , 28 ] . As a result, the literature often provides simple descriptive accounts of influencing factors, producing fragmented research results. This study aims to bridge these gaps through quantitative research examining factors influencing older adults’ willingness to use MVCS from their perspectives, addressing three key research questions. First, which individual, family, and social factors influence older adults’ decisions to accept or reject MVCS? Second, how does the relative importance of each factor differ in affecting their willingness to use these services? Third, what specific content preferences do older adults have regarding MVCS? To address these questions, the study adopts Anderson’s health service model, which categorizes determinants into predisposing characteristics, enabling resources, and need factors [ 29 , 30 ] , and uses these dimensions to classify individual variables potentially affecting older adults’ willingness to use MVCS. This framework was integrated with the Shapley value method, derived from cooperative game theory, to quantify the contribution of each factor across predisposing characteristics, enabling resources, and demand factors [ 31 ] . This approach provides a robust theoretical basis for identifying demographic, behavioral, and need-based characteristics of older adults with a high propensity to use MVCS, enabling evidence-based targeting of interventions and resource allocation. Furthermore, a self-developed questionnaire was used to assess older adults’ specific content preferences for MVCS. This instrument enables a nuanced understanding of multifaceted needs and supports the development of tailored service improvements. These findings can inform the development of MVCS globally, particularly in countries facing similar aging-related healthcare access challenges. By clarifying the critical factors influencing older adults' engagement with MVCS and their service preferences, this research provides evidence to support targeted policy-making aimed at optimizing MVCS and reducing healthcare disparities. Methods Data sources A descriptive cross-sectional study was conducted among older adults in Zhejiang Province, China, between January and May 2024. A multistage stratified random sampling approach was employed. In the first stage, cities in Zhejiang Province were stratified by geographic region and socioeconomic development indicators. From these strata, six cities were randomly selected for questionnaire distribution: Hangzhou, Wenzhou, Shaoxing, Jiaxing, Lishui, and Quzhou. In the second stage, communities within each selected city were further stratified into three socioeconomic tiers (high, medium, and low) based on objective indicators, including median household income, infrastructure quality, and commercial accessibility. Older adults were then randomly sampled from each tier. Participants were eligible if they met the following inclusion criteria: (1) Age ≥ 60 years; (2) Free from severe cognitive impairment, life-threatening illnesses, or terminal diseases; (3) Able to communicate independently and complete the questionnaire; (4) Provided written informed consent. Participants were excluded if they met any of the following criteria: (1) Diagnosis of mental disorders that preclude normal communication; (2) Refusal to participate. The cross-sectional sample size was calculated using the standard formula: \(\:N=\frac{{Z}_{\alpha\:}^{2}}{{\delta\:}^{2}}p（1-p）\) [ 32 ] . Given the absence of prior research on this specific population, the proportion was conservatively set at 0.5. With the allowable error of 0.05, the initial sample size was calculated as 385. The target sample size was increased to 462 to account for a 20% attrition rate due to potential non-response or inaccurate data. Ultimately, a total of 510 participants were recruited. Sixteen questionnaires were excluded due to invalidity caused by missing information. The final number of valid questionnaires was 494, yielding an effective response rate of 96.86%. All investigators received standardized training prior to data collection to ensure consistent survey administration. After verifying eligibility and obtaining informed consent, trained volunteers or family members assisted illiterate older adults or those who had difficulty completing questionnaires during face-to-face interviews, thereby ensuring equitable participation and data accuracy. Measurements Outcome variable Willingness to use MVCS Willingness to use MVCS was assessed using a 5-point Likert scale. This scale was designed to capture the participants' subjective attitudes towards using MVCS. Response options ranged from 1 (“strongly unwilling”) to 5 (“strongly willing”): 1 = strongly unwilling, 2 = somewhat unwilling, 3 = unclear, 4 = somewhat willing, and 5 = strongly willing. Independent variables This study was based on Ronald M. Anderson’s framework [ 29 ] , which posits that healthcare utilization is influenced by predisposing, enabling, and need factors. This framework guided the analysis of participants’ willingness to engage with MVCS. Predisposing characteristics Age: Young-old (60–69 years), middle-old (70–79 years), and oldest-old (≥ 80 years). Gender: Male or female. Education level: Primary school or below, secondary school, high school, junior college, and higher education. Marital status: Never married/divorced, married, and widowed. Residential status: Living alone, with a spouse, with adult children, with both a spouse and adult children, and other. Number of children: 0, 1, 2, or ≥ 3. Enabling resources Self-rated ease of medical visits: Assessed using a 5-point Likert scale (1 = very poor, 5 = very good), reflecting perceived ease of navigating healthcare services. MVCS awareness: Measured on a 5-point scale (1 = completely unclear, 5 = very clear), evaluating participants’ understanding of MVCS functions. Monthly family income (RMB): Categorized as 8000. Family care: Assessed using the Chinese version of the Family APGAR Index (APGAR) [ 33 ] , which contains five items on a 3-point Likert scale. Total scores range from 0 to 10, with the higher scores indicating better family support. Cronbach’s α in this study was 0.743. Social support: Measured using the Social Support Rating Scale (SSRS) [ 34 ] , which includes 10 items with a total score ranging from 12 to 66. Score ≤ 22 indicate poor social support, 23–44 moderate support, and 45–66 adequate support. The SSRS comprises three dimensions: objective support, subjective support, and support utilization. Cronbach’s α coefficient in this study was 0.735. Need factors Medical visit autonomy: A binary variable indicating whether the participant could complete medical visits independently (yes/no). Individualized accompaniment demands: Assessed using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree), reflecting preferences for personalized assistance during medical visits. Self-rated health status: Measured on a 5-point scale (1 = excellent, 5 = very poor), capturing participants’ subjective perception of their health. Chronic disease: A binary variable indicating the presence of any self-reported chronic condition (yes/no). MVCS development prospects: Measured on a 5-point scale (1 = no development potential, 5 = broad development potential), gauging perceived sustainability of MVCS. Need for assistance from MVCS: Assessed using a self-developed 15-item scale informed by literature review and expert consultations [ 19 , 20 , 23 – 25 ] . The scale covers assistance needs such as guidance during examinations, appointments, and taking medicines. Items were rated on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree), yielding total scores ranging from 15 to 75 (see Table 2 ). The instrument demonstrated high reliability, with test-retest reliability of 0.838 and Cronbach’s α of 0.917. Table 2 Specific needs for assistance from medical visit companion services among participants (n = 494). Variables Need (%) Item Mean ± SD Rank Evaluation of MVCS 472 (95.5) 4.15 ± 0.93 1 assistance in navigating medical examinations 450 (91.1) 4.12 ± 1.01 2 Medical appointment scheduling 409 (82.8) 3.90 ± 1.22 3 Taking medicines in the hospital 417 (84.4) 3.90 ± 1.13 4 Accompaniment during medical visits 418 (84.6) 3.89 ± 1.15 5 Psychological support 434 (87.9) 3.88 ± 1.06 6 Hospital selection tailored to the patient's condition 399 (80.4) 3.79 ± 1.23 7 Instructions on the utilization of MVCS 418 (84.6) 3.79 ± 1.19 8 Provision of disease management information 417 (84.4) 3.78 ± 1.10 9 Assistance in communication with medical professionals 403 (81.6) 3.76 ± 1.21 10 Guidance for patients on rational medication use 388 (78.5) 3.71 ± 1.29 11 Picking up and dropping off patients 368 (74.5) 3.66 ± 1.31 12 Scheduling of medical visit timing 373 (75.5) 3.48 ± 1.28 13 Home delivery of medications 346 (70.2) 3.31 ± 1.28 14 Assisting in accessing telehealth services 437 (70.2) 3.23 ± 1.38 15 Total score 56.36 ± 12.13 Note: "Need" refers to respondents who selected "Strongly Agree" or "Agree". Statistical method To ensure data accuracy, dual-entry validation was performed by two independent researchers using Excel 2019, with discrepancies resolved through cross-checking. Data processing and analysis were conducted using SPSS 27.0 and Stata 18.0, encompassing screening, cleaning, statistical analysis, and tabulation. The willingness to use MVCS was taken as the dependent variable in both univariate analysis and regression analysis. Although the dependent variable was not normally distributed, the assumption of normality of the residuals was verified. (The verification results of the normality of the residuals are detailed in the supplementary-1). Descriptive statistics are presented as frequencies and percentages (n, %) for categorical variables and as means with standard deviations (SD) for continuous variables. Given the categorical nature of the dependent variable and the non-normal distribution of continuous independent variables, appropriate non-parametric statistical methods were employed. Chi-square tests (χ 2 ) were utilized to examine associations between categorical independent variables and the dependent variable. For continuous independent variables, Kruskal-Wallis H tests ( H ) were applied to assess differences in their distributions across the categories of the dependent variable. Ordinal and continuous variables were analyzed using Spearman correlation coefficients, and variables with p < 0.05 were retained for subsequent modeling. Multicollinearity was assessed via variance inflation factors (VIF) in Stata 18.0, with all variables exhibiting VIF values 0.05), supporting the appropriateness of the model. Guided by the Anderson health service model, significant variables were categorized into three theoretical dimensions: X 1 (predisposing characteristics), X 2 (enabling resources), and X 3 (need factors). The regression equation was specified as follows: \(\:\text{ln}\left(\frac{P\left(y\le\:j\right)}{P\left(y>j\right)}\right)={\theta\:}_{j}-({\beta\:}_{0}+{\beta\:}_{1}{X}_{1}+{\beta\:}_{2}{X}_{2}+{\beta\:}_{3}{X}_{3})\) , where y represents willingness to use MVCS with "strongly willing" set as the reference group for comparison. Model's fit was assessed using chisquare goodness-of-fit test and the pseudo-R² statistic. The Shapley value method, based on regression analysis, was applied to quantify the contribution of each dimension and its constituent variables [ 36 ] . The analysis was conducted in Stata18.0 using the command: shapley2, stat (r2) group (X1, X2, X3). The output provided the Shapley value and its corresponding percentage contributions [ 36 ] . Categorical variables were appropriately coded before inclusion in the analysis. This approach enables the quantification of each factor's relative importance in explaining the variance in the dependent variable. Results Sample characteristics A total of 494 individuals were enrolled in the present study. The characteristics of study participants are presented in Table 1 . The mean age of the participants was 68.38 (SD = 7.15) years. Of the 494 participants, 254 (51.4%) were male and 240 (48.6%) were female. Regarding education, 47.4% had completed primary school or below. The majority (87%) were married, and 44.3% had two children. Approximately 30.8% of households reported monthly incomes between 6001 and 8000 yuan (approximately USD 826.48 to 1,101.78). Regarding health status, 51.5% rated their health as good or very good, while 66.7% reported having chronic diseases. Concerning medical-related factors, 48.8% reported being unable to complete medical visits independently, while 65.2% considered them convenient or very convenient. Additionally, 84.6% had moderate social support (Social Support Rating Scale score 23–44), and 73.1% exhibited high family care (Family APGAR Index ≥ 7). Table 1 Comparisons of willingness to use medical visit companion service among participants by sociodemographic characteristics (n = 494). Variables Total (n = 494) Willingness score (Mean ± SD) χ² / H p-value Predisposing characteristics Age (years) 60–69 280 (56.7) 3.42 ± 1.22 22.545 0.004 70–79 163 (33.0) 3.20 ± 1.23 80~ 51 (10.3) 2.71 ± 1.19 Gender Male 254 (51.4) 3.21 ± 1.27 10.148 0.038 Female 240 (48.6) 3.34 ± 1.19 Education Primary school or below 234 (47.4) 3.01 ± 1.22 54.515 < 0.001 Secondary school 146 (29.6) 3.35 ± 1.18 High school 67 (13.6) 3.97 ± 1.11 Junior college 33 (6.7) 3.33 ± 1.19 Higher education 14 (2.8) 3.43 ± 1.28 Marital status Never married or divorced 15 (3.0) 3.60 ± 1.18 7.405 0.494 Married 430 (87.0) 3.25 ± 1.24 Widowed 49 (9.9) 3.35 ± 1.20 Residential status Live alone 52 (10.5) 3.52 ± 1.23 15.985 0.192 Live with a spouse 250 (50.6) 3.32 ± 1.23 Live with adult children 82 (16.6) 3.02 ± 1.25 Live with both a spouse and adult children 110 (22.3) 3.25 ± 1.22 Number of children 0 6 (1.2) 3.67 ± 1.03 47.385 < 0.001 1 186 (37.7) 3.68 ± 1.23 2 219 (44.3) 3.10 ± 1.19 ≥ 3 83 (16.8) 2.81 ± 1.11 Table 1 (continued) Variables Total (n = 494) Willingness score (Mean ± SD) χ² / H p-value Enabling resources Self-rated ease of medical visits Very convenient 124 (25.1) 2.88 ± 1.19 54.642 < 0.001 Convenient 198 (40.1) 3.21 ± 1.19 Average 103 (20.9) 3.64 ± 1.22 Inconvenient 52 (10.5) 3.56 ± 1.27 Very inconvenient 17 (3.4) 3.76 ± 1.09 MVCS awareness Very clear 10 (2.0) 4.40 ± 1.08 91.930 < 0.001 Clear 73 (14.8) 3.86 ± 1.10 Moderate 76 (15.4) 3.51 ± 1.08 Unclear 195 (39.5) 2.98 ± 1.17 Completely unclear 140 (28.3) 3.16 ± 1.31 Social support Low (≤ 22) 4 (0.8) 3.25 ± 1.50 22.847 a < 0.001 Moderate (23–44) 418 (84.6) 3.35 ± 1.23 High (≥ 45) 72 (14.6) 2.82 ± 1.14 Family care Low (0–3) 17 (3.4) 3.29 ± 1.49 58.117 a < 0.001 Moderate (4–6) 116 (23.5) 3.72 ± 1.12 High (7–10) 361 (73.1) 3.13 ± 1.22 Monthly family income in RMB ≤ 2000 65 (13.2) 2.92 ± 1.29 28.577 0.027 2001–4000 93 (18.8) 3.31 ± 1.22 4001–6000 140 (28.3) 3.22 ± 1.14 6001–8000 152 (30.8) 3.34 ± 1.28 > 8000 44 (5.3) 3.66 ± 1.22 Need factors Medical visits autonomy Yes 253 (51.2) 3.15 ± 1.28 11.450 0.022 No 241 (48.8) 3.41 ± 1.17 Table 1 (continued) Variables Total (n = 494) Willingness score (Mean ± SD) χ² / H p-value Individualized accompaniment demands Strongly agree 198 (40.1) 3.51 ± 1.25 120.688 <0.001 Agree 195 (39.5) 3.31 ± 1.14 General 70 (20.2) 2.84 ± 1.20 Disagree 26 (5.3) 2.69 ± 1.23 Strongly disagree 5 (1.0) 1.80 ± 1.79 Self-rated health status Very good 66 (13.4) 2.95 ± 1.25 85.831 <0.001 Good 188 (38.1) 2.88 ± 1.17 General 143 (28.9) 3.42 ± 1.12 Poor 81 (16.4) 4.01 ± 1.16 Very poor 16 (3.2) 4.19 ± 0.91 Chronic diseases Yes 329 (66.7) 3.37 ± 1.24 11.085 0.026 No 165 (33.4) 3.07 ± 1.21 Need for assistance from MVCS Low (<35) 33 (6.7) 2.33 ± 1.32 42.739 a <0.001 Moderate (35-54) 144 (29.1) 2.94 ± 1.15 High (≥55) 317 (64.2) 3.52 ± 1.18 MVCS development prospects Broad development potential 92 (18.6) 4.12 ± 1.15 109.848 <0.001 Good development potential 245 (49.6) 3.25 ± 1.16 Moderate development potential 118 (23.9) 2.87 ± 1.14 Limited development potential 28 (5.7) 2.50 ± 1.04 No development potential 11 (2.2) 2.91 ± 1.22 Note: “a” represents Kruskal-Wallis H tests. Utilization status of medical visit companion services among older adults in Zhejiang province The mean willingness to use MVCS among older adults was 3.27 ± 1.23. Of the 494 participants, 166 (33.6%) expressed their intention to use the service, 177 (35.8%) indicated their unwillingness to use it, and 151 (30.6%) reported a neutral stance. Furthermore, 67.8% of participants were unclear about the service, whereas only 16.8% demonstrated a clear understanding. A chi-square test revealed a significant association between willingness (χ² = 91.211, p < 0.001), indicating that greater awareness is strongly associated with higher willingness. Table 2 summarizes the specific content demands for MVCS. The highest-rated demand was for evaluation of MVCS (M = 4.15 ± 0.93), followed by assistance in navigating medical examinations (M = 4.12 ± 1.01), and medical appointment scheduling (M = 3.90 ± 1.22). Conversely, need for scheduling medical visit timing (M = 3.48 ± 1.28), home delivery of medications (M = 3.31 ± 1.28), and assistance with accessing telehealth services (M = 2.23 ± 1.38) was relatively lower. Results of analysis on factors associated with the utilization of medical visit companion service among older adults Table 1 presents the results of correlation analysis between the independent and dependent variables. The findings indicate that a combination of predisposing characteristics, enabling resources, and need factors significantly influenced older adults’ willingness to use MVCS. Among the predisposing characteristics, age and gender were statistically significant factors ( p < 0.05). Additionally, education level, and number of children showed highly significant associations ( p < 0.001). Regarding enabling resources, social support and monthly family income were significantly associated with MVCS willingness ( p < 0.05), while self-rated ease of medical visits, MVCS awareness, and family care were strongly associated ( p < 0.001). Within the need factors, both medical visits autonomy and the presence of chronic diseases were statistically significant ( p < 0.05). Moreover, individualized accompaniment demands, self-rated health status, the perceived need for MVCS, and expectation for MVCS development exhibited highly significant associations ( p < 0.001). Detailed results are provided in Supplementary Tables 1 and 2. Table 1 summarizes descriptive statistics for variables associated with willingness to use MVCS. Table 2 presents correlations between specific content preferences for MVCS and utilization willingness. Results of logistic regression analysis of willingness to use medical visit companion service among older adults Table 3 presents the results of the multicollinearity analysis among the independent variables. All variance expansion factor (VIF) values were below 5, indicating no serious multicollinearity among the independent variables [ 35 ] . The chi-square goodness-of-fit test yielded a statistic of 230.827 (df = 16), with a Nagelkerke R² of 0.399 and a Cox & Snell R² of 0.373. These indicators suggest that the model has good explanatory power and fits the data well. Table 3 also reports the results of the logistic regression models. Among predisposing characteristics, participants aged 60–69 years were less likely to use MVCS compared with those aged 70–79 (EXP (B) = 0.653, 95% CI 0.444 ~ 0.960). Willingness to use MVCS decreased significantly with increasing age. Having a high school education was associated with a significantly greater willingness to use MVCS compared to primary school or below (EXP (B) = 3.592, 95% CI 2.066 ~ 6.243), suggesting that higher educational attainment increases acceptance. No significant associations were observed for other education levels. Regarding enabling resources, willingness to use MVCS decreased with higher self-rated ease of medical visits (B = -0.341, p < 0.001) and increased with greater social support (B = -0.070, p < 0.001).‌ In contrast, MVCS awareness was positively associated with willingness (B = 0.296, p = 0.001), while monthly family income was not significantly associated. As for need factors, willingness to use MVCS increased with higher perceived need for assistance (B = 0.042, p < 0.001).‌ Conversely, better self-rated health was inversely associated with willingness (B = -0.478, p < 0.001), as was medical visit autonomy (B = -0.436, p = 0.018). Table 3 Ordered logistic regression of factors associated with older adults’ willingness to use medical visit companion services (n = 494). Factor Variable B SE Wald p -value EXP (B) 95% CI VIF Predisposing characteristics Age (Control group: 60–69) 1.156 70–79 -1.124 0.333 11.436 0.030 0.653 0.444 ~ 0.960 ≥ 80 -0.426 0.197 4.696 < 0.001 0.325 0.169 ~ 0.623 Education (Control group: Primary school or below) 1.138 Secondary school 0.253 0.212 1.426 0.232 1.288 0.850 ~ 1.950 High school 1.279 0.282 20.551 < 0.001 3.592 2.066 ~ 6.243 Junior college 0.067 0.381 0.031 0.861 1.069 0.507 ~ 2.253 Higher education 0.124 0.542 0.052 0.819 1.132 0.392 ~ 3.273 Number of children (Control group: 1) 1.189 0 -0.788 0.766 1.058 0.304 0.455 0.101 ~ 2.041 2 -0.808 0.206 15.460 < 0.001 0446 0.298 ~ 0.667 ≥ 3 -1.103 0.282 15.242 < 0.001 0.332 0.191 ~ 0.577 Enabling resources Self-rated ease of medical visits -0.341 0.089 14.857 < 0.001 0.711 0.598 ~ 0.846 1.100 MVCS awareness 0.296 0.091 10.613 0.001 1.345 1.125 ~ 1.608 1.210 Social support -0.070 0.016 18.038 < 0.001 0.933 0.903 ~ 0.963 1.078 Need factors Medical visit autonomy(Control group: Non-independent medical visits) 1.120 -0.436 0.185 5.563 0.018 0.646 0.450 ~ 0.929 Individualized accompaniment demands 0.349 0.114 9.338 0.002 1.418 1.133 ~ 1.774 1.313 Self-rated health status -0.478 0.098 24.045 < 0.001 0.620 0.512 ~ 0.750 1.195 Need for assistance from MVCS 0.042 0.009 23.551 < 0.001 1.043 1.025 ~ 1.061 1.263 Shapley value decomposition of factors associated with older adults’ willingness to use medical visit companion services The Shapley value decomposition, based on the Anderson health service model, revealed the distinct contributions of its three theoretical dimensions to the variance in older adults’ willingness to use MVCS. Need factors were the most influential, accounting for 49.73% of the total variance, followed by enabling resources (26.90%) and predisposing characteristics (24.02%). Among the specific predictors, self-rated health status (21.29%), need for assistance from MVCS (15.56%), number of children (11.67%), MVCS awareness (9.40%), and individualized accompaniment demands (9.13%) were identified as the most significant contributors. Together, these factors explained the majority of the variance in willingness to use MVCS among older adults. Table 4 provides a detailed breakdown of the contribution of each variable. Table 4 Shapley value decomposition results of factors influencing older adults’ willingness to use medical visit companion services. Factor Variable Shapley Contribution Percentage Predisposing characteristics Age 0.0115 7.43% 24.02% Education level 0.0076 4.92% Number of children 0.0183 11.67% Enabling resources Self-rated healthcare convenience 0.0138 8.96% 26.90% MVCS awareness 0.0145 9.40% Social support 0.0138 8.90% Need factors Medical care autonomy 0.0042 2.75% 48.73% Individualized accompaniment demands 0.0141 9.13% Self-rated health status 0.0330 21.29% The need for assistance from MVCS 0.0240 15.56% Total 0.1545 100% Discussion Willingness of older adults to use medical visit companion services The study found that 33.6% of older adults expressed willingness to use MVCS, which is broadly consistent with findings by Xu et al. [ 23 ] and Ma et al. [ 37 ] , who reported willingness rates of 38.5% and 40.34%, respectively. However, this figure is markedly lower than the willingness observed among younger generations. For instance, studies by Zhou et al. [ 28 ] and Bai et al. [ 27 ] found that 68.98% and 71.42% of adult children, respectively, expressed willingness to choose or recommend MVCS for their parents. This marked intergenerational discrepancy suggests differing perceptions of MVCS across age cohorts. Two key factors may explain this divergence. First, MVCS remains an emerging and underdeveloped service in China, with limited professionalization and inconsistent quality standards. Older adults, who generally have lower tolerance for uncertainty, may feel hesitant to engage with such unstandardized services. [ 23 , 25 ] . In contrast, younger adults, who are more familiar with new service models and more adaptive to technological change, are less deterred by these uncertainties [ 38 ] . Second, deeply-rooted cultural norms regarding eldercare in China continue to shape attitudes towards caregiving [ 39 ] . The traditional concept of “filial piety” maintains that children should provide direct care and accompany their aging parents during medical visits, as a core demonstration of familial responsibility. This cultural expectation creates tension when MVCS is introduced as a substitute for direct familial involvement. Although many adult children face work-related constraints that hinder their ability to accompany parents to medical visits [ 28 ] , the use of MVCS may be perceived by older adults as a sign of neglect or insufficient filial devotion. Notably, the meaning of filial piety appears to be evolving across generations. Among younger adults, professionalized care services such as MVCS are increasingly framed not as a replacement for filial duties, but as a modern extension of them, as they provide quality care in situations where direct involvement is not feasible [ 40 ] . These services are viewed as a responsible and even superior alternative to untrained familial support. However, many older adults have not internalized this redefinition. From their perspective, reliance on paid companions may imply a breakdown in traditional family obligations and be perceived as symbolic of familial abandonment [ 41 ] . This intergenerational gap in cultural interpretation constitutes a substantial barrier to MVCS acceptance among older adults. Bridging these gaps will require not only the professionalization of MVCS itself but also public education efforts that foster a shared understanding of how such services can complement, rather than contradict, traditional caregiving values. Needs for assistance from medical visit companion services among older adults Older adults exhibited both high and varied levels of needs for assistance from MVCS. Their need is especially concentrated in basic in-hospital companionship services, such as scheduling medical appointments, accompanying patients during medical visits, and providing procedural guidance. In contrast, their need for extended out-of-hospital services, such as telehealth access, home medications delivery, and assistance in scheduling medical visits, was relatively low. These patterns are consistent with findings by Xu et al., who reported that older adults’ needs for MVCS primarily focus on in-hospital assistance [ 23 ] . Similarly, prior studies have shown that most older patients prioritize the smooth execution of the medical visit process [ 42 ] . Vedel et al. further emphasized that in-hospital operations are directly associated with the successful completion of medical visits, especially for older adults navigating complex care pathways [ 43 ] . These operational steps, ranging from consultation to diagnosis, play a critical role in shaping older adults’ healthcare experiences. In contrast, extended out-of-hospital services are often designed to support self-management, facilitate communication with providers, and assist in care transitions [ 44 , 45 ] . However, since these services do not directly intervene in core medical procedures such as examination or diagnosis, they may fall outside the immediate priorities of older adults, contributing to their relatively lower demand. Given these findings, it is recommended that MVCS providers prioritize addressing older adults' in-hospital service needs. Once these needs are adequately met, service provision may gradually expand to incorporate out-of-hospital components. Enhancing the quality of both service domains will help ensure that MVCS can adapt to the evolving and diverse care needs of the aging population. Influencing factors of willingness to use medical visit companion services among older adults Guided by Anderson’s health behavior model, this study emphasized that older adults’ willingness to engage with MVCS must be examined across multiple dimensions, namely, predisposing characteristics, enabling resources, and need factors. Wang et al. [ 26 ] , using an extended TPB/TAM model, examined psychological and cognitive factors influencing individuals' acceptance of MVCS. Compared with this framework, Anderson’s model incorporates a broader range of factors, including individual, familial, social, and resource-related dimensions. In addition to accounting for individual cognitive factors, Anderson’s model identifies how objective resource constraints influence older adults’ healthcare behaviors. This study applies the Anderson model to disentangle heterogeneous factors affecting the willingness to use MVCS among older adults and to delineate the profiles of individuals most inclined to use these services. As a result, the model has been widely adopted in medical service research due to its robust explanatory power. Need factors, as defined in Anderson’s health behavior model, reflect both older adults’ perceived health problems and their actual needs for health services. In our study, need factors emerged as the most influential dimension. Notably, self-rated health status was the strongest individual predictor, contributing 21.29% to the total explanatory power. A significant negative association was observed between self-rated health status and willingness to use MVCS. This finding aligns with Zhao et al. [ 46 ] , who noted that lower perceived health is associated with increased healthcare utilization. Self-perceived health is influenced not only by an individual's health condition but also by their subjective interpretation of disease severity. Previous studies suggest that individuals with chronic but non-life-threatening illnesses often overestimate their health status compared to those with severe, life-threatening conditions [ 47 ] . This overestimation may lead to the underutilization of supportive services such as MVCS. Therefore, enhancing older adults' health literacy and correcting mismatches between perceived and actual health status are crucial steps in promoting rational MVCS use. MVCS should be tailored to the needs of older adults with lower self-perceived health, who are more likely to seek such support. However, the service should not be limited solely to this group. Even those with relatively high self-perceived health may face functional barriers or need occasional assistance, and thus should also be included within the target population. Effective service targeting requires an understanding of both subjective and objective indicators of need. In addition to perceived health, this study examined concrete service contents. Two factors were found to be especially influential: the need for assistance from MVCS (15.56%) and the demand for individualized accompaniment (9.13%). A greater intensity of need in either area was strongly associated with increased willingness to use MVCS. These findings are consistent with the core assumptions of Anderson’s model, which posits that recognized need acts as a critical motivator for service utilization [ 48 , 49 ] . Specifically, the demand for individualized accompaniment reflects older adults’ higher-order expectations for quality, dignity, and personalization in healthcare, which are values central to the philosophy of patient-centered care (PCC). PCC emphasizes care that is respectful of and responsive to individual patient preferences, needs, and values [ 50 ] . Empirical evidence suggests that embedding PCC principles in service design can significantly enhance patient engagement [ 51 ] . Despite widespread efforts to improve technical service quality, many providers still underemphasize patient-centered principles, potentially limiting service uptake. Therefore, MVCS providers should prioritize personalized accompaniment throughout the medical visit process, tailoring support to individuals’ health status, care-seeking behaviors, and psychosocial contexts. Additionally, establishing standardized, age-appropriate service need assessment systems will be essential for refining MVCS implementation and ensuring evidence-based, user-centered delivery. Enabling resources act as a critical intermediary, translating perceived need into actual service utilization behavior. In this study, enabling resources ranked second only to need factors in influencing willingness to use MVCS. Among these, service awareness had a particularly significant impact. As MVCS remains an emerging concept in China, only 16.8% of older adults reported being aware of such services. Accurate awareness is strongly associated with increased willingness to engage with MVCS, whereas misconceptions significantly undermine this intention [ 52 ] . Due to insufficient public information and regulatory ambiguity, MVCS is often mistakenly associated with illicit actors such as appointment scalpers or unauthorized drug vendors [ 23 , 42 ] , leading to heightened public vigilance. Even individuals inclined to use such services may be discouraged by fears of fraud or potential breaches of medical privacy. Therefore, efforts to promote MVCS must emphasize clear, accurate communication to dispel misunderstandings and build public trust. Another key enabling factor is self-rated ease of medical visits, which reflects an individual’s perceived ease of accessing medical services [ 53 ] . This variable encompasses both system-level elements (e.g., service availability, affordability) and personal factors (e.g., functional ability, navigation skills) [ 54 ] . Older adults who perceive healthcare to be readily accessible tend to exhibit lower demand for MVCS, likely due to confidence in their ability to manage medical visits independently. In contrast, individuals facing access barriers-whether systemic or personal-report significantly greater willingness to rely on MVCS. Our study quantified the explanatory power of this variable at 8.96%, underscoring its substantive role in shaping service engagement. Social support emerged as another vital enabling resource. This study measured support across three dimensions: objective support, subjective support, and support utilization. Findings revealed that older adults with lower levels of social support demonstrated a significantly higher likelihood of using MVCS. These findings align with Cui et al. [ 55 ] , who emphasized that family and peer support influence the uptake of community-based care services. Specifically, limited objective support indicates unmet companionship needs; weak subjective support reflects emotional isolation; and poor support utilization implies difficulty in accessing available help [ 34 ] . MVCS, by design, addresses all three deficiencies through practical assistance, emotional companionship, and resource linkage during the medical visit process. In the Chinese context, older adults’ social support systems are often centered around their children [ 56 ] . Our study further found that older adults with fewer children were more likely to use MVCS, offering indirect evidence that family structure is tightly linked to service reliance. Taken together, these findings highlight the need to position MVCS not only as a healthcare facilitator but also as a compensatory social support mechanism for vulnerable elderly populations. Predisposing characteristics refer to factors that precede an individual's interaction with healthcare services and influence their health-related attitudes and behaviors. These can be categorized into ascribed characteristics (e.g., age and number of children) and acquired characteristics (e.g., education level). Among ascribed characteristics, both the number of children and age significantly influence older adults’ willingness to use MVCS. Numerous studies have shown that healthcare utilization often declines with age [ 57 , 58 ] , which aligns with our findings. However, other studies have reported a positive association between age and healthcare demand [ 59 ] . This discrepancy may stem from limited exposure to traditional norms, such as reliance on family accompaniment and skepticism toward assistance from unfamiliar individuals. Xu et al. noted that the older generations’ lived experiences and limited acceptance of modern values make them more inclined to rely on traditional care models and resistant to emerging services like MVCS [ 60 ] . These contextual and cultural factors merit further investigation, particularly in settings with strong intergenerational caregiving norms. Regarding acquired characteristics, education level plays a similarly significant role. Our study found that older adults with higher levels of education were more willing to engage with MVCS, a finding consistent with previous research. [ 60 ] . A likely explanation is that better-educated individuals are more open to new concepts and have broader access to information. As a result, they tend to have higher expectations for personalized or diversified medical services, making them more receptive to emerging healthcare innovations like MVCS. Understanding these predisposing characteristics allows for the development of predictive profiles to identify subgroups of older adults who are more likely to accept MVCS. This, in turn, can enhance service targeting, improve outreach efficiency, and inform evidence-based allocation of healthcare resources. Limitations This study has several limitations. First, it focused solely on older adults' willingness to use MVCS, without incorporating their willingness to pay. As a market-based service, pricing can significantly influence actual utilization, and future studies should integrate willingness to pay as a critical factor. Second, although this study explored older adults’ need for MVCS, it did not fully capture the breadth of potential service needs. Given the evolving nature of MVCS in China, where service content continues to expand, there may be inconsistencies between the predefined needs assessed in this study and the actual services currently offered. Third, the generalizability of our findings is limited by the geographic and sample scope, as participants were drawn solely from Zhejiang Province, and the sample size was relatively small. Lastly, future research should broaden the sample base and explore additional influencing factors, including those shaped by cultural norms, willingness to pay, or the perspectives of adult children, which may play a pivotal role in decision-making regarding elder care. Despite these limitations, our study provides a valuable foundation for future investigations and offers practical insights for improving the adoption and design of medical visit companion services. Conclusions This study revealed that older adults in China demonstrated relatively low willingness to use medical visit companion services (MVCS). Willingness was significantly shaped by predisposing characteristics, enabling resources, and, most notably, need factors. Among these, self-rated health status, perceived need for assistance, MVCS awareness, and levels of family and social support emerged as the most influential determinants. MVCS can serve as a critical support mechanism for older adults who lack sufficient family or social assistance, particularly in the context of an aging population and increasing prevalence of empty-nest households. To promote greater MVCS adoption, three priority areas should be addressed. First, community-based health education and disease-specific literacy campaigns should be expanded to improve older adults’ awareness of their health conditions and encourage proactive care-seeking behaviors. Second, service provider should improve their capacity to identify older adults with high service needs and deliver tailored, patient-centered support that aligns with individual health profiles. Third, comprehensive communication strategies—leveraging governmental, institutional, and academic networks—should be implemented to improve public trust and awareness of MVCS. Multi-platform outreach via traditional and digital media can enhance service visibility and encourage broader uptake. Together, these efforts can bridge the gap between service availability and actual utilization, providing a roadmap for optimizing eldercare delivery in rapidly aging societies. Declarations Acknowledgments The authors thank all the subjects for their participation. Authors’ contributions CG, YC, and JX initiated the study. CG, YC, JX, JM, LZ, and DL contributed to its design. DL YC, and LT managed the data collection, with DL and YC managing data curation JX, JM, and LZ performed the data analysis. JX wrote the first draft of the manuscript. CG and YC critically revised the paper. All authors reviewed critically subsequent drafts of the manuscript and approved its final manuscript. Funding This paper is supported by the National Social Science Foundation of China [grant numbers 24CSH134]. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate All methods were performed in accordance with the Declarations of Helsinki. Participation was voluntary and after the provision of written informed consent. The data were anonymized and participants were assured of confidentiality. 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This demographic shift has substantially increased the demand for long-term healthcare services. In China, there is a marked mismatch between the allocation of healthcare resources and regional needs. High-quality medical resources are disproportionately concentrated in tertiary hospitals and urban areas, whereas primary healthcare institutions and rural regions face severe shortages\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. This imbalance has exacerbated challenges for older adults seeking high-quality care\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Digital healthcare services have played a pivotal role in addressing these challenges and supporting the goal of \"medical care for the aged\"\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. However, the implementation of digital healthcare services faces numerous challenges. One of the most pressing issues is the digital divide affecting older adults. A study by Li et al. involving 155,695 participants found that internet use rates among adults aged 60 years and older were 5.56% in China, compared with 39.37% in Mexico, and 58.01% in the United States\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. These findings indicate that older adults constitute a prototypical digitally vulnerable group within digital healthcare. They also frequently experience cognitive impairments and physical limitations when engaging with digital healthcare services\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. This disparity directly limits older adults\u0026rsquo; access to digital healthcare and exacerbates existing healthcare inequalities\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. To overcome these challenges, older adults often rely on external assistance, particularly from adult children\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, such support networks are frequently inadequate. Research based on the US Census and the University of Michigan Health and Retirement Study has shown that approximately 22% of adults aged 65 and older lack accessible kin, a legally designated surrogate, or a caregiver\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Similar patterns are observed in China\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. These limited support networks are insufficient to meet older adults\u0026rsquo; needs for medical companionship. Addressing the medical companionship needs of older adults is a global public health priority that requires collaboration across families, governments, and societies.\u003c/p\u003e\u003cp\u003eMedical companionship originated from the US Patient Navigation (PN) model\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, which is defined as an individualized intervention in cancer care aimed at addressing access barriers and facilitating timely healthcare utilization, diagnosis, and treatment. PN roles require specialized training for specific health issues, as oncology care coordination necessitates advanced clinical expertise\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The PN model has gradually been expanded to various chronic diseases (e.g., diabetes, HIV infection, cardiovascular disease, chronic kidney disease, and dementia) to improve patient outcomes\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. This model has undergone significant local adaptation within the Chinese healthcare system. In China, medical companionship is referred to as medical visit companion services (MVCS). Against the backdrop of rapid population aging and imbalanced distribution of healthcare resources, MVCS are evolving in China to address the growing demand for socialized long-term care among older adults and to bridge gaps in healthcare access\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. MVCS refer to assistance provided by trained non-professional caregivers to three main groups: older adults, pregnant women, and individuals with mobility impairments or limited capacity to access healthcare independently\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. MVCS involve trained non-professional caregivers who provide non-clinical support, including practical assistance (scheduling appointments, navigating departments, retrieving medications, arranging hospital admissions), as well as psychosocial support to facilitate doctor-patient communication and offer emotional assistance\u003csup\u003e[\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This approach helps older adults better understand medical information and enhances the effectiveness of their medical visits. It also addresses the practical problem of limited access to medical care\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, MVCS remain in the early stages of development and face several challenges. In 2025, the Shanghai Civil Affairs Bureau launched \u003cem\u003ethe Pilot Program for Assisted Medical Visit Companion Services for Older Adults\u003c/em\u003e, aiming to enhance the professionalism and standardization of the service. The program outlines measures to standardize service protocols, develop specialized companion teams, and reinforce institutional safeguards\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. These initiatives directly address the issues of service quality and operational efficiency, laying the groundwork for broader implementation.\u003c/p\u003e\u003cp\u003eExisting studies have primarily employed qualitative methods to explore older adults' willingness to use MVCS and their specific service needs\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Only a limited number of studies have used quantitative methods to investigate older adults' willingness to engage with MVCS. However, these studies often lack a theoretical framework, which limits the rigor and generalizability of their findings\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Most quantitative studies have examined MVCS from the perspective of adult children. Although this approach facilitates data collection, the absence of older adults' perspectives introduces inherent bias into the findings\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. These gaps hinder the in-depth exploration of factors influencing older adults' willingness to use MVCS and prevent medical companions from identifying those most likely to engage with such services. Consequently, resource allocation remains inefficient. Moreover, no previous study has examined older adults\u0026rsquo; specific content preferences for MVCS, limiting the development of tailored service designs. Additionally, most researchers have investigated influencing factors directly, without categorizing them, assessing their relative importance, or grounding their studies in theoretical frameworks\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. As a result, the literature often provides simple descriptive accounts of influencing factors, producing fragmented research results. This study aims to bridge these gaps through quantitative research examining factors influencing older adults\u0026rsquo; willingness to use MVCS from their perspectives, addressing three key research questions. First, which individual, family, and social factors influence older adults\u0026rsquo; decisions to accept or reject MVCS? Second, how does the relative importance of each factor differ in affecting their willingness to use these services? Third, what specific content preferences do older adults have regarding MVCS? To address these questions, the study adopts Anderson\u0026rsquo;s health service model, which categorizes determinants into predisposing characteristics, enabling resources, and need factors\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, and uses these dimensions to classify individual variables potentially affecting older adults\u0026rsquo; willingness to use MVCS. This framework was integrated with the Shapley value method, derived from cooperative game theory, to quantify the contribution of each factor across predisposing characteristics, enabling resources, and demand factors\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. This approach provides a robust theoretical basis for identifying demographic, behavioral, and need-based characteristics of older adults with a high propensity to use MVCS, enabling evidence-based targeting of interventions and resource allocation. Furthermore, a self-developed questionnaire was used to assess older adults\u0026rsquo; specific content preferences for MVCS. This instrument enables a nuanced understanding of multifaceted needs and supports the development of tailored service improvements. These findings can inform the development of MVCS globally, particularly in countries facing similar aging-related healthcare access challenges. By clarifying the critical factors influencing older adults' engagement with MVCS and their service preferences, this research provides evidence to support targeted policy-making aimed at optimizing MVCS and reducing healthcare disparities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData sources\u003c/h2\u003e\u003cp\u003eA descriptive cross-sectional study was conducted among older adults in Zhejiang Province, China, between January and May 2024. A multistage stratified random sampling approach was employed. In the first stage, cities in Zhejiang Province were stratified by geographic region and socioeconomic development indicators. From these strata, six cities were randomly selected for questionnaire distribution: Hangzhou, Wenzhou, Shaoxing, Jiaxing, Lishui, and Quzhou. In the second stage, communities within each selected city were further stratified into three socioeconomic tiers (high, medium, and low) based on objective indicators, including median household income, infrastructure quality, and commercial accessibility. Older adults were then randomly sampled from each tier. Participants were eligible if they met the following inclusion criteria: (1) Age\u0026thinsp;\u0026ge;\u0026thinsp;60 years; (2) Free from severe cognitive impairment, life-threatening illnesses, or terminal diseases; (3) Able to communicate independently and complete the questionnaire; (4) Provided written informed consent. Participants were excluded if they met any of the following criteria: (1) Diagnosis of mental disorders that preclude normal communication; (2) Refusal to participate.\u003c/p\u003e\u003cp\u003eThe cross-sectional sample size was calculated using the standard formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N=\\frac{{Z}_{\\alpha\\:}^{2}}{{\\delta\\:}^{2}}p（1-p）\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e\u003cb\u003e[\u003c/b\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Given the absence of prior research on this specific population, the proportion was conservatively set at 0.5. With the allowable error of 0.05, the initial sample size was calculated as 385. The target sample size was increased to 462 to account for a 20% attrition rate due to potential non-response or inaccurate data. Ultimately, a total of 510 participants were recruited. Sixteen questionnaires were excluded due to invalidity caused by missing information. The final number of valid questionnaires was 494, yielding an effective response rate of 96.86%.\u003c/p\u003e\u003cp\u003eAll investigators received standardized training prior to data collection to ensure consistent survey administration. After verifying eligibility and obtaining informed consent, trained volunteers or family members assisted illiterate older adults or those who had difficulty completing questionnaires during face-to-face interviews, thereby ensuring equitable participation and data accuracy.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eOutcome variable\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003eWillingness to use MVCS\u003c/h2\u003e\u003cp\u003eWillingness to use MVCS was assessed using a 5-point Likert scale. This scale was designed to capture the participants' subjective attitudes towards using MVCS. Response options ranged from 1 (\u0026ldquo;strongly unwilling\u0026rdquo;) to 5 (\u0026ldquo;strongly willing\u0026rdquo;): 1\u0026thinsp;=\u0026thinsp;strongly unwilling, 2\u0026thinsp;=\u0026thinsp;somewhat unwilling, 3\u0026thinsp;=\u0026thinsp;unclear, 4\u0026thinsp;=\u0026thinsp;somewhat willing, and 5\u0026thinsp;=\u0026thinsp;strongly willing.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eIndependent variables\u003c/h3\u003e\n\u003cp\u003eThis study was based on Ronald M. Anderson\u0026rsquo;s framework\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, which posits that healthcare utilization is influenced by predisposing, enabling, and need factors. This framework guided the analysis of participants\u0026rsquo; willingness to engage with MVCS.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePredisposing characteristics\u003c/h2\u003e\u003cp\u003eAge: Young-old (60\u0026ndash;69 years), middle-old (70\u0026ndash;79 years), and oldest-old (\u0026ge;\u0026thinsp;80 years).\u003c/p\u003e\u003cp\u003eGender: Male or female.\u003c/p\u003e\u003cp\u003eEducation level: Primary school or below, secondary school, high school, junior college, and higher education.\u003c/p\u003e\u003cp\u003eMarital status: Never married/divorced, married, and widowed.\u003c/p\u003e\u003cp\u003eResidential status: Living alone, with a spouse, with adult children, with both a spouse and adult children, and other.\u003c/p\u003e\u003cp\u003eNumber of children: 0, 1, 2, or \u0026ge;\u0026thinsp;3.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEnabling resources\u003c/h3\u003e\n\u003cp\u003eSelf-rated ease of medical visits: Assessed using a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;very poor, 5\u0026thinsp;=\u0026thinsp;very good), reflecting perceived ease of navigating healthcare services.\u003c/p\u003e\u003cp\u003eMVCS awareness: Measured on a 5-point scale (1\u0026thinsp;=\u0026thinsp;completely unclear, 5\u0026thinsp;=\u0026thinsp;very clear), evaluating participants\u0026rsquo; understanding of MVCS functions.\u003c/p\u003e\u003cp\u003eMonthly family income (RMB): Categorized as \u0026lt;\u0026thinsp;2000, 2001\u0026ndash;4000, 4001\u0026ndash;6000, 6001\u0026ndash;8000, and \u0026gt;\u0026thinsp;8000.\u003c/p\u003e\u003cp\u003eFamily care: Assessed using the \u003cem\u003eChinese version of the Family APGAR Index\u003c/em\u003e (APGAR)\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, which contains five items on a 3-point Likert scale. Total scores range from 0 to 10, with the higher scores indicating better family support. Cronbach\u0026rsquo;s α in this study was 0.743.\u003c/p\u003e\u003cp\u003eSocial support: Measured using the \u003cem\u003eSocial Support Rating Scale\u003c/em\u003e (SSRS)\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, which includes 10 items with a total score ranging from 12 to 66. Score\u0026thinsp;\u0026le;\u0026thinsp;22 indicate poor social support, 23\u0026ndash;44 moderate support, and 45\u0026ndash;66 adequate support. The SSRS comprises three dimensions: objective support, subjective support, and support utilization. Cronbach\u0026rsquo;s α coefficient in this study was 0.735.\u003c/p\u003e\n\u003ch3\u003eNeed factors\u003c/h3\u003e\n\u003cp\u003eMedical visit autonomy: A binary variable indicating whether the participant could complete medical visits independently (yes/no).\u003c/p\u003e\u003cp\u003eIndividualized accompaniment demands: Assessed using a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree), reflecting preferences for personalized assistance during medical visits.\u003c/p\u003e\u003cp\u003eSelf-rated health status: Measured on a 5-point scale (1\u0026thinsp;=\u0026thinsp;excellent, 5\u0026thinsp;=\u0026thinsp;very poor), capturing participants\u0026rsquo; subjective perception of their health.\u003c/p\u003e\u003cp\u003eChronic disease: A binary variable indicating the presence of any self-reported chronic condition (yes/no).\u003c/p\u003e\u003cp\u003eMVCS development prospects: Measured on a 5-point scale (1\u0026thinsp;=\u0026thinsp;no development potential, 5\u0026thinsp;=\u0026thinsp;broad development potential), gauging perceived sustainability of MVCS.\u003c/p\u003e\u003cp\u003eNeed for assistance from MVCS: Assessed using a self-developed 15-item scale informed by literature review and expert consultations\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The scale covers assistance needs such as guidance during examinations, appointments, and taking medicines. Items were rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree), yielding total scores ranging from 15 to 75 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The instrument demonstrated high reliability, with test-retest reliability of 0.838 and Cronbach\u0026rsquo;s α of 0.917.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpecific needs for assistance from medical visit companion services among participants (n\u0026thinsp;=\u0026thinsp;494).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeed (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eItem Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvaluation of MVCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e472 (95.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eassistance in navigating medical examinations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e450 (91.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical appointment scheduling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e409 (82.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaking medicines in the hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e417 (84.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccompaniment during medical visits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e418 (84.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e434 (87.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital selection tailored to the patient's condition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e399 (80.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstructions on the utilization of MVCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e418 (84.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvision of disease management information\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e417 (84.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssistance in communication with medical professionals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e403 (81.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuidance for patients on rational medication use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e388 (78.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePicking up and dropping off patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e368 (74.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScheduling of medical visit timing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e373 (75.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome delivery of medications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e346 (70.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssisting in accessing telehealth services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e437 (70.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e56.36\u0026thinsp;\u0026plusmn;\u0026thinsp;12.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: \"Need\" refers to respondents who selected \"Strongly Agree\" or \"Agree\".\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical method\u003c/h2\u003e\u003cp\u003eTo ensure data accuracy, dual-entry validation was performed by two independent researchers using Excel 2019, with discrepancies resolved through cross-checking. Data processing and analysis were conducted using SPSS 27.0 and Stata 18.0, encompassing screening, cleaning, statistical analysis, and tabulation. The willingness to use MVCS was taken as the dependent variable in both univariate analysis and regression analysis. Although the dependent variable was not normally distributed, the assumption of normality of the residuals was verified. (The verification results of the normality of the residuals are detailed in the supplementary-1). Descriptive statistics are presented as frequencies and percentages (n, %) for categorical variables and as means with standard deviations (SD) for continuous variables. Given the categorical nature of the dependent variable and the non-normal distribution of continuous independent variables, appropriate non-parametric statistical methods were employed. Chi-square tests (χ\u003csup\u003e2\u003c/sup\u003e) were utilized to examine associations between categorical independent variables and the dependent variable. For continuous independent variables, Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e tests (\u003cem\u003eH\u003c/em\u003e) were applied to assess differences in their distributions across the categories of the dependent variable. Ordinal and continuous variables were analyzed using Spearman correlation coefficients, and variables with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were retained for subsequent modeling. Multicollinearity was assessed via variance inflation factors (VIF) in Stata 18.0, with all variables exhibiting VIF values\u0026thinsp;\u0026lt;\u0026thinsp;5, indicating no severe collinearity\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. The proportional odds assumption for ordinal logistic regression was tested using the parallel lines test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), supporting the appropriateness of the model. Guided by the Anderson health service model, significant variables were categorized into three theoretical dimensions: X\u003csub\u003e1\u003c/sub\u003e (predisposing characteristics), X\u003csub\u003e2\u003c/sub\u003e (enabling resources), and X\u003csub\u003e3\u003c/sub\u003e (need factors). The regression equation was specified as follows: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{ln}\\left(\\frac{P\\left(y\\le\\:j\\right)}{P\\left(y\u0026gt;j\\right)}\\right)={\\theta\\:}_{j}-({\\beta\\:}_{0}+{\\beta\\:}_{1}{X}_{1}+{\\beta\\:}_{2}{X}_{2}+{\\beta\\:}_{3}{X}_{3})\\)\u003c/span\u003e\u003c/span\u003e, where y represents willingness to use MVCS with \"strongly willing\" set as the reference group for comparison. Model's fit was assessed using chisquare goodness-of-fit test and the pseudo-R\u0026sup2; statistic. The Shapley value method, based on regression analysis, was applied to quantify the contribution of each dimension and its constituent variables\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003cb\u003e]\u003c/b\u003e\u003c/sup\u003e. The analysis was conducted in Stata18.0 using the command: shapley2, stat (r2) group (X1, X2, X3). The output provided the Shapley value and its corresponding percentage contributions\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003cb\u003e]\u003c/b\u003e\u003c/sup\u003e. Categorical variables were appropriately coded before inclusion in the analysis. This approach enables the quantification of each factor's relative importance in explaining the variance in the dependent variable.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSample characteristics\u003c/h2\u003e\u003cp\u003eA total of 494 individuals were enrolled in the present study. The characteristics of study participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the participants was 68.38 (SD\u0026thinsp;=\u0026thinsp;7.15) years. Of the 494 participants, 254 (51.4%) were male and 240 (48.6%) were female. Regarding education, 47.4% had completed primary school or below. The majority (87%) were married, and 44.3% had two children. Approximately 30.8% of households reported monthly incomes between 6001 and 8000 yuan (approximately USD 826.48 to 1,101.78). Regarding health status, 51.5% rated their health as good or very good, while 66.7% reported having chronic diseases. Concerning medical-related factors, 48.8% reported being unable to complete medical visits independently, while 65.2% considered them convenient or very convenient. Additionally, 84.6% had moderate social support (Social Support Rating Scale score 23\u0026ndash;44), and 73.1% exhibited high family care (Family APGAR Index\u0026thinsp;\u0026ge;\u0026thinsp;7).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparisons of willingness to use medical visit companion service among participants by sociodemographic characteristics (n\u0026thinsp;=\u0026thinsp;494).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;494)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWillingness score\u003c/p\u003e\u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; / H\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredisposing characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e280 (56.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e70\u0026ndash;79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e163 (33.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e80~\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e254 (51.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e240 (48.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e234 (47.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e146 (29.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigher education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married or divorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e430 (87.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidential status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLive alone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLive with a spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e250 (50.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLive with adult children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLive with both a spouse and adult children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e110 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e186 (37.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e219 (44.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(continued)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;494)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWillingness score\u003c/p\u003e\u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; / H\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnabling resources\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eSelf-rated ease of medical visits\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery convenient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124 (25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConvenient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198 (40.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103 (20.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInconvenient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery inconvenient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMVCS awareness\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery clear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnclear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195 (39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompletely unclear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow (\u0026le;\u0026thinsp;22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.847\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate (23\u0026ndash;44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e418 (84.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh (\u0026ge;\u0026thinsp;45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow (0\u0026ndash;3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.117\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate (4\u0026ndash;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh (7\u0026ndash;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e361 (73.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly family income in RMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2001\u0026ndash;4000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4001\u0026ndash;6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6001\u0026ndash;8000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e152 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;8000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeed factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMedical visits autonomy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e253 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e241 (48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cp\u003eTable 1 (continued)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"621\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n = 494)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWillingness score\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026chi;\u0026sup2; / H\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 615px;\"\u003e\n \u003cp\u003eIndividualized accompaniment\u0026nbsp;demands\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eStrongly agree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e198 (40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3.51\u0026nbsp;\u0026plusmn;\u0026nbsp;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e120.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e195 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3.31\u0026nbsp;\u0026plusmn;\u0026nbsp;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e70 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.84\u0026nbsp;\u0026plusmn;\u0026nbsp;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e26 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.69\u0026nbsp;\u0026plusmn;\u0026nbsp;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eStrongly\u0026nbsp;disagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e5 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.80\u0026nbsp;\u0026plusmn;\u0026nbsp;1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eSelf-rated health status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eVery good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e66 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.95 \u0026plusmn; 1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e85.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e188 (38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.88 \u0026plusmn; 1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e143 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3.42 \u0026plusmn; 1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e81 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4.01 \u0026plusmn; 1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eVery poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e16 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4.19 \u0026plusmn; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eChronic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e329 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3.37 \u0026plusmn; 1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e11.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e165 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3.07 \u0026plusmn; 1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 615px;\"\u003e\n \u003cp\u003eNeed for assistance from MVCS\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eLow (\u0026lt;35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e33 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.33 \u0026plusmn; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e42.739\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eModerate (35-54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e144 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.94 \u0026plusmn; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eHigh (\u0026ge;55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e317 (64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3.52 \u0026plusmn; 1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 615px;\"\u003e\n \u003cp\u003eMVCS development prospects\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eBroad development\u0026nbsp;potential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e92 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4.12 \u0026plusmn; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e109.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eGood development potential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e245 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3.25 \u0026plusmn; 1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eModerate development potential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e118 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.87 \u0026plusmn; 1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eLimited development potential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e28 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.50 \u0026plusmn; 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003eNo development potential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e11 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.91 \u0026plusmn; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u0026ldquo;a\u0026rdquo; represents\u0026nbsp;Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e tests.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eUtilization status of medical visit companion services among older adults in Zhejiang province\u003c/h2\u003e\u003cp\u003eThe mean willingness to use MVCS among older adults was 3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23. Of the 494 participants, 166 (33.6%) expressed their intention to use the service, 177 (35.8%) indicated their unwillingness to use it, and 151 (30.6%) reported a neutral stance. Furthermore, 67.8% of participants were unclear about the service, whereas only 16.8% demonstrated a clear understanding. A chi-square test revealed a significant association between willingness (χ\u0026sup2; = 91.211, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that greater awareness is strongly associated with higher willingness. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the specific content demands for MVCS. The highest-rated demand was for evaluation of MVCS (M\u0026thinsp;=\u0026thinsp;4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93), followed by assistance in navigating medical examinations (M\u0026thinsp;=\u0026thinsp;4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01), and medical appointment scheduling (M\u0026thinsp;=\u0026thinsp;3.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22). Conversely, need for scheduling medical visit timing (M\u0026thinsp;=\u0026thinsp;3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28), home delivery of medications (M\u0026thinsp;=\u0026thinsp;3.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28), and assistance with accessing telehealth services (M\u0026thinsp;=\u0026thinsp;2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38) was relatively lower.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults of analysis on factors associated with the utilization of medical visit companion service among older adults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of correlation analysis between the independent and dependent variables. The findings indicate that a combination of predisposing characteristics, enabling resources, and need factors significantly influenced older adults\u0026rsquo; willingness to use MVCS. Among the predisposing characteristics, age and gender were statistically significant factors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, education level, and number of children showed highly significant associations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding enabling resources, social support and monthly family income were significantly associated with MVCS willingness (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while self-rated ease of medical visits, MVCS awareness, and family care were strongly associated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Within the need factors, both medical visits autonomy and the presence of chronic diseases were statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, individualized accompaniment demands, self-rated health status, the perceived need for MVCS, and expectation for MVCS development exhibited highly significant associations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Detailed results are provided in Supplementary Tables\u0026nbsp;1 and 2. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes descriptive statistics for variables associated with willingness to use MVCS. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents correlations between specific content preferences for MVCS and utilization willingness.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults of logistic regression analysis of willingness to use medical visit companion service among older adults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the multicollinearity analysis among the independent variables. All variance expansion factor (VIF) values were below 5, indicating no serious multicollinearity among the independent variables\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. The chi-square goodness-of-fit test yielded a statistic of 230.827 (df\u0026thinsp;=\u0026thinsp;16), with a Nagelkerke R\u0026sup2; of 0.399 and a Cox \u0026amp; Snell R\u0026sup2; of 0.373. These indicators suggest that the model has good explanatory power and fits the data well. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e also reports the results of the logistic regression models. Among predisposing characteristics, participants aged 60\u0026ndash;69 years were less likely to use MVCS compared with those aged 70\u0026ndash;79 (EXP (B)\u0026thinsp;=\u0026thinsp;0.653, 95% CI 0.444\u0026thinsp;~\u0026thinsp;0.960). Willingness to use MVCS decreased significantly with increasing age. Having a high school education was associated with a significantly greater willingness to use MVCS compared to primary school or below (EXP (B)\u0026thinsp;=\u0026thinsp;3.592, 95% CI 2.066\u0026thinsp;~\u0026thinsp;6.243), suggesting that higher educational attainment increases acceptance. No significant associations were observed for other education levels. Regarding enabling resources, willingness to use MVCS decreased with higher self-rated ease of medical visits (B = -0.341, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and increased with greater social support (B = -0.070, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u0026zwnj; In contrast, MVCS awareness was positively associated with willingness (B\u0026thinsp;=\u0026thinsp;0.296, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), while monthly family income was not significantly associated. As for need factors, willingness to use MVCS increased with higher perceived need for assistance (B\u0026thinsp;=\u0026thinsp;0.042, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u0026zwnj; Conversely, better self-rated health was inversely associated with willingness (B = -0.478, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as was medical visit autonomy (B = -0.436, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOrdered logistic regression of factors associated with older adults\u0026rsquo; willingness to use medical visit companion services (n\u0026thinsp;=\u0026thinsp;494).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eEXP (B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003ePredisposing characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e\u003cp\u003eAge (Control group: 60\u0026ndash;69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u0026ndash;79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.444\u0026thinsp;~\u0026thinsp;0.960\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.169\u0026thinsp;~\u0026thinsp;0.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e\u003cp\u003eEducation (Control group: Primary school or below)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e1.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.850\u0026thinsp;~\u0026thinsp;1.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e2.066\u0026thinsp;~\u0026thinsp;6.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJunior college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e1.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.507\u0026thinsp;~\u0026thinsp;2.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e1.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.392\u0026thinsp;~\u0026thinsp;3.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNumber of children (Control group: 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.101\u0026thinsp;~\u0026thinsp;2.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.298\u0026thinsp;~\u0026thinsp;0.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.191\u0026thinsp;~\u0026thinsp;0.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEnabling resources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-rated ease of medical visits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.598\u0026thinsp;~\u0026thinsp;0.846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMVCS awareness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e1.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e1.125\u0026thinsp;~\u0026thinsp;1.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.903\u0026thinsp;~\u0026thinsp;0.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeed factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e\u003cp\u003eMedical visit autonomy(Control group: Non-independent medical visits)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.120\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.450\u0026thinsp;~\u0026thinsp;0.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndividualized accompaniment\u003c/p\u003e\u003cp\u003edemands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e1.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e1.133\u0026thinsp;~\u0026thinsp;1.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.313\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-rated health status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.512\u0026thinsp;~\u0026thinsp;0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeed for assistance from MVCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e1.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e1.025\u0026thinsp;~\u0026thinsp;1.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.263\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eShapley value decomposition of factors associated with older adults\u0026rsquo; willingness to use medical visit companion services\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Shapley value decomposition, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ebased on\u003c/span\u003e the Anderson health service model, revealed the distinct contributions of its three theoretical dimensions to the variance in older adults\u0026rsquo; willingness to use MVCS. Need factors were the most influential, accounting for 49.73% of the total variance, followed by enabling resources (26.90%) and predisposing characteristics (24.02%). Among the specific predictors, self-rated health status (21.29%), need for assistance from MVCS (15.56%), number of children (11.67%), MVCS awareness (9.40%), and individualized accompaniment demands (9.13%) were identified as the most significant contributors. Together, these factors explained the majority of the variance in willingness to use MVCS among older adults. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a detailed breakdown of the contribution of each variable.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eShapley value decomposition results of factors influencing older adults\u0026rsquo; willingness to use medical visit companion services.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShapley\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContribution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePredisposing characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.43%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e24.02%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.92%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEnabling resources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-rated healthcare convenience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.96%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.90%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMVCS awareness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eNeed factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical care autonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48.73%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndividualized accompaniment demands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.13%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-rated health status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.29%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe need for assistance from MVCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eWillingness of older adults to use medical visit companion services\u003c/h2\u003e\u003cp\u003eThe study found that 33.6% of older adults expressed willingness to use MVCS, which is broadly consistent with findings by Xu et al.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003eand Ma et al.\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, who reported willingness rates of 38.5% and 40.34%, respectively. However, this figure is markedly lower than the willingness observed among younger generations. For instance, studies by Zhou et al.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e and Bai et al.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e found that 68.98% and 71.42% of adult children, respectively, expressed willingness to choose or recommend MVCS for their parents. This marked intergenerational discrepancy suggests differing perceptions of MVCS across age cohorts. Two key factors may explain this divergence. First, MVCS remains an emerging and underdeveloped service in China, with limited professionalization and inconsistent quality standards. Older adults, who generally have lower tolerance for uncertainty, may feel hesitant to engage with such unstandardized services.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. In contrast, younger adults, who are more familiar with new service models and more adaptive to technological change, are less deterred by these uncertainties\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Second, deeply-rooted cultural norms regarding eldercare in China continue to shape attitudes towards caregiving\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. The traditional concept of \u0026ldquo;filial piety\u0026rdquo; maintains that children should provide direct care and accompany their aging parents during medical visits, as a core demonstration of familial responsibility. This cultural expectation creates tension when MVCS is introduced as a substitute for direct familial involvement. Although many adult children face work-related constraints that hinder their ability to accompany parents to medical visits\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, the use of MVCS may be perceived by older adults as a sign of neglect or insufficient filial devotion. Notably, the meaning of filial piety appears to be evolving across generations. Among younger adults, professionalized care services such as MVCS are increasingly framed not as a replacement for filial duties, but as a modern extension of them, as they provide quality care in situations where direct involvement is not feasible\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. These services are viewed as a responsible and even superior alternative to untrained familial support. However, many older adults have not internalized this redefinition. From their perspective, reliance on paid companions may imply a breakdown in traditional family obligations and be perceived as symbolic of familial abandonment\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. This intergenerational gap in cultural interpretation constitutes a substantial barrier to MVCS acceptance among older adults. Bridging these gaps will require not only the professionalization of MVCS itself but also public education efforts that foster a shared understanding of how such services can complement, rather than contradict, traditional caregiving values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eNeeds for assistance from medical visit companion services among older adults\u003c/h2\u003e\u003cp\u003eOlder adults exhibited both high and varied levels of needs for assistance from MVCS. Their need is especially concentrated in basic in-hospital companionship services, such as scheduling medical appointments, accompanying patients during medical visits, and providing procedural guidance. In contrast, their need for extended out-of-hospital services, such as telehealth access, home medications delivery, and assistance in scheduling medical visits, was relatively low. These patterns are consistent with findings by Xu et al., who reported that older adults\u0026rsquo; needs for MVCS primarily focus on in-hospital assistance\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Similarly, prior studies have shown that most older patients prioritize the smooth execution of the medical visit process\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Vedel et al. further emphasized that in-hospital operations are directly associated with the successful completion of medical visits, especially for older adults navigating complex care pathways\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. These operational steps, ranging from consultation to diagnosis, play a critical role in shaping older adults\u0026rsquo; healthcare experiences. In contrast, extended out-of-hospital services are often designed to support self-management, facilitate communication with providers, and assist in care transitions\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. However, since these services do not directly intervene in core medical procedures such as examination or diagnosis, they may fall outside the immediate priorities of older adults, contributing to their relatively lower demand. Given these findings, it is recommended that MVCS providers prioritize addressing older adults' in-hospital service needs. Once these needs are adequately met, service provision may gradually expand to incorporate out-of-hospital components. Enhancing the quality of both service domains will help ensure that MVCS can adapt to the evolving and diverse care needs of the aging population.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eInfluencing factors of willingness to use medical visit companion services among older adults\u003c/h2\u003e\u003cp\u003eGuided by Anderson\u0026rsquo;s health behavior model, this study emphasized that older adults\u0026rsquo; willingness to engage with MVCS must be examined across multiple dimensions, namely, predisposing characteristics, enabling resources, and need factors. Wang et al.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, using an extended TPB/TAM model, examined psychological and cognitive factors influencing individuals' acceptance of MVCS. Compared with this framework, Anderson\u0026rsquo;s model incorporates a broader range of factors, including individual, familial, social, and resource-related dimensions. In addition to accounting for individual cognitive factors, Anderson\u0026rsquo;s model identifies how objective resource constraints influence older adults\u0026rsquo; healthcare behaviors. This study applies the Anderson model to disentangle heterogeneous factors affecting the willingness to use MVCS among older adults and to delineate the profiles of individuals most inclined to use these services. As a result, the model has been widely adopted in medical service research due to its robust explanatory power.\u003c/p\u003e\u003cp\u003eNeed factors, as defined in Anderson\u0026rsquo;s health behavior model, reflect both older adults\u0026rsquo; perceived health problems and their actual needs for health services. In our study, need factors emerged as the most influential dimension. Notably, self-rated health status was the strongest individual predictor, contributing 21.29% to the total explanatory power. A significant negative association was observed between self-rated health status and willingness to use MVCS. This finding aligns with Zhao et al.\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, who noted that lower perceived health is associated with increased healthcare utilization. Self-perceived health is influenced not only by an individual's health condition but also by their subjective interpretation of disease severity. Previous studies suggest that individuals with chronic but non-life-threatening illnesses often overestimate their health status compared to those with severe, life-threatening conditions\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. This overestimation may lead to the underutilization of supportive services such as MVCS. Therefore, enhancing older adults' health literacy and correcting mismatches between perceived and actual health status are crucial steps in promoting rational MVCS use. MVCS should be tailored to the needs of older adults with lower self-perceived health, who are more likely to seek such support. However, the service should not be limited solely to this group. Even those with relatively high self-perceived health may face functional barriers or need occasional assistance, and thus should also be included within the target population. Effective service targeting requires an understanding of both subjective and objective indicators of need. In addition to perceived health, this study examined concrete service contents. Two factors were found to be especially influential: the need for assistance from MVCS (15.56%) and the demand for individualized accompaniment (9.13%). A greater intensity of need in either area was strongly associated with increased willingness to use MVCS. These findings are consistent with the core assumptions of Anderson\u0026rsquo;s model, which posits that recognized need acts as a critical motivator for service utilization\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Specifically, the demand for individualized accompaniment reflects older adults\u0026rsquo; higher-order expectations for quality, dignity, and personalization in healthcare, which are values central to the philosophy of patient-centered care (PCC). PCC emphasizes care that is respectful of and responsive to individual patient preferences, needs, and values\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Empirical evidence suggests that embedding PCC principles in service design can significantly enhance patient engagement\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Despite widespread efforts to improve technical service quality, many providers still underemphasize patient-centered principles, potentially limiting service uptake. Therefore, MVCS providers should prioritize personalized accompaniment throughout the medical visit process, tailoring support to individuals\u0026rsquo; health status, care-seeking behaviors, and psychosocial contexts. Additionally, establishing standardized, age-appropriate service need assessment systems will be essential for refining MVCS implementation and ensuring evidence-based, user-centered delivery.\u003c/p\u003e\u003cp\u003eEnabling resources act as a critical intermediary, translating perceived need into actual service utilization behavior. In this study, enabling resources ranked second only to need factors in influencing willingness to use MVCS. Among these, service awareness had a particularly significant impact. As MVCS remains an emerging concept in China, only 16.8% of older adults reported being aware of such services. Accurate awareness is strongly associated with increased willingness to engage with MVCS, whereas misconceptions significantly undermine this intention\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Due to insufficient public information and regulatory ambiguity, MVCS is often mistakenly associated with illicit actors such as appointment scalpers or unauthorized drug vendors\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, leading to heightened public vigilance. Even individuals inclined to use such services may be discouraged by fears of fraud or potential breaches of medical privacy. Therefore, efforts to promote MVCS must emphasize clear, accurate communication to dispel misunderstandings and build public trust. Another key enabling factor is self-rated ease of medical visits, which reflects an individual\u0026rsquo;s perceived ease of accessing medical services \u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. This variable encompasses both system-level elements (e.g., service availability, affordability) and personal factors (e.g., functional ability, navigation skills)\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Older adults who perceive healthcare to be readily accessible tend to exhibit lower demand for MVCS, likely due to confidence in their ability to manage medical visits independently. In contrast, individuals facing access barriers-whether systemic or personal-report significantly greater willingness to rely on MVCS. Our study quantified the explanatory power of this variable at 8.96%, underscoring its substantive role in shaping service engagement. Social support emerged as another vital enabling resource. This study measured support across three dimensions: objective support, subjective support, and support utilization. Findings revealed that older adults with lower levels of social support demonstrated a significantly higher likelihood of using MVCS. These findings align with Cui et al.\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e, who emphasized that family and peer support influence the uptake of community-based care services. Specifically, limited objective support indicates unmet companionship needs; weak subjective support reflects emotional isolation; and poor support utilization implies difficulty in accessing available help\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. MVCS, by design, addresses all three deficiencies through practical assistance, emotional companionship, and resource linkage during the medical visit process. In the Chinese context, older adults\u0026rsquo; social support systems are often centered around their children\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. Our study further found that older adults with fewer children were more likely to use MVCS, offering indirect evidence that family structure is tightly linked to service reliance. Taken together, these findings highlight the need to position MVCS not only as a healthcare facilitator but also as a compensatory social support mechanism for vulnerable elderly populations.\u003c/p\u003e\u003cp\u003ePredisposing characteristics refer to factors that precede an individual's interaction with healthcare services and influence their health-related attitudes and behaviors. These can be categorized into ascribed characteristics (e.g., age and number of children) and acquired characteristics (e.g., education level). Among ascribed characteristics, both the number of children and age significantly influence older adults\u0026rsquo; willingness to use MVCS. Numerous studies have shown that healthcare utilization often declines with age\u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e, which aligns with our findings. However, other studies have reported a positive association between age and healthcare demand\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. This discrepancy may stem from limited exposure to traditional norms, such as reliance on family accompaniment and skepticism toward assistance from unfamiliar individuals. Xu et al. noted that the older generations\u0026rsquo; lived experiences and limited acceptance of modern values make them more inclined to rely on traditional care models and resistant to emerging services like MVCS\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. These contextual and cultural factors merit further investigation, particularly in settings with strong intergenerational caregiving norms. Regarding acquired characteristics, education level plays a similarly significant role. Our study found that older adults with higher levels of education were more willing to engage with MVCS, a finding consistent with previous research.\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. A likely explanation is that better-educated individuals are more open to new concepts and have broader access to information. As a result, they tend to have higher expectations for personalized or diversified medical services, making them more receptive to emerging healthcare innovations like MVCS. Understanding these predisposing characteristics allows for the development of predictive profiles to identify subgroups of older adults who are more likely to accept MVCS. This, in turn, can enhance service targeting, improve outreach efficiency, and inform evidence-based allocation of healthcare resources.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, it focused solely on older adults' willingness to use MVCS, without incorporating their willingness to pay. As a market-based service, pricing can significantly influence actual utilization, and future studies should integrate willingness to pay as a critical factor. Second, although this study explored older adults\u0026rsquo; need for MVCS, it did not fully capture the breadth of potential service needs. Given the evolving nature of MVCS in China, where service content continues to expand, there may be inconsistencies between the predefined needs assessed in this study and the actual services currently offered. Third, the generalizability of our findings is limited by the geographic and sample scope, as participants were drawn solely from Zhejiang Province, and the sample size was relatively small. Lastly, future research should broaden the sample base and explore additional influencing factors, including those shaped by cultural norms, willingness to pay, or the perspectives of adult children, which may play a pivotal role in decision-making regarding elder care. Despite these limitations, our study provides a valuable foundation for future investigations and offers practical insights for improving the adoption and design of medical visit companion services.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study revealed that older adults in China demonstrated relatively low willingness to use medical visit companion services (MVCS). Willingness was significantly shaped by predisposing characteristics, enabling resources, and, most notably, need factors. Among these, self-rated health status, perceived need for assistance, MVCS awareness, and levels of family and social support emerged as the most influential determinants. MVCS can serve as a critical support mechanism for older adults who lack sufficient family or social assistance, particularly in the context of an aging population and increasing prevalence of empty-nest households. To promote greater MVCS adoption, three priority areas should be addressed. First, community-based health education and disease-specific literacy campaigns should be expanded to improve older adults\u0026rsquo; awareness of their health conditions and encourage proactive care-seeking behaviors. Second, service provider should improve their capacity to identify older adults with high service needs and deliver tailored, patient-centered support that aligns with individual health profiles. Third, comprehensive communication strategies\u0026mdash;leveraging governmental, institutional, and academic networks\u0026mdash;should be implemented to improve public trust and awareness of MVCS. Multi-platform outreach via traditional and digital media can enhance service visibility and encourage broader uptake. Together, these efforts can bridge the gap between service availability and actual utilization, providing a roadmap for optimizing eldercare delivery in rapidly aging societies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the subjects for their participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCG, YC, and JX initiated the study. CG, YC, JX, JM, LZ, and DL contributed to its design. DL YC, and LT managed the data collection, with DL and YC managing data curation JX, JM, and LZ performed the data analysis. JX wrote the first draft of the manuscript. CG and YC critically revised the paper. All authors reviewed critically subsequent drafts of the manuscript and approved its final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper is supported by the National Social Science Foundation of China [grant numbers 24CSH134].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were performed in accordance with the Declarations of Helsinki. Participation was voluntary and after the provision of written informed consent. The data were anonymized and participants were assured of confidentiality. Ethical approval was obtained from the \u003cstrong\u003eWenzhou Medical University (NO.2023-002).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eSchool of Nursing, Wenzhou Medical University, Wenzhou, China.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChina Statistical Bureau. 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BMC Geriatr. 2024;24(1):17.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Older adult, Medical visit companion services utilization, Anderson’s model, Shapley value method, Influencing factors","lastPublishedDoi":"10.21203/rs.3.rs-7211539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7211539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe rapid digital transformation of healthcare, coupled with the increasing prevalence of empty-nest families in China, has widened disparities in healthcare access among older adults, especially in navigating technology-dependent services. Medical visit companion services (MVCS) may help address these challenges and reduce pressure on the healthcare system. However, as an emerging service in China, MVCS remain insufficiently studied regarding older adults' willingness to engage with them and the factors influencing this willingness. This study aimed to assess older adults\u0026rsquo; willingness to use MVCS and to identify and quantify the determinants.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eDrawing on Anderson\u0026rsquo;s health behavior model, we developed an analytical framework incorporating predisposing characteristics, enabling resources, and need factors to examine older adults\u0026rsquo; willingness to use MVCS in China. Cross-sectional data from 494 participants in Zhejiang Province were analyzed using χ\u0026sup2; tests, \u003cem\u003eH\u003c/em\u003e tests, ordered logistic regression, and Shapley value decomposition to identify factors influencing MVCS willingness and to quantify their contributions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe mean score for willingness to use MVCS was 3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23. Higher education level, MVCS awareness, monthly family income, individual accompaniment demand, and perceived need for assistance were positively associated with willingness. Older age, greater self-rated ease of medical visits, stronger social support, better self-rated health, and higher medical visit autonomy were negatively associated. Shapley value decomposition indicated that need factors (48.73%) were the primary drivers of willingness, with self-rated health (21.29%) as the most predictive. Enabling resources (26.90%) and predisposing characteristics (24.02%) also contributed substantially, together explaining most of the variance in willingness.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe willingness of Chinese older adults to engage with MVCS is influenced by predisposing characteristics, enabling resources, and need factors, with the need factors demonstrating the strongest explanatory power. To enhance older adults\u0026rsquo; willingness to utilize MVCS and promote sustainable services development, improving health literacy may strengthen their capacity to recognize health problems and articulate their actual needs. Service providers should be equipped to identify older adults most likely to engage with MVCS. Tailoring services to meet diverse needs is essential to optimize MVCS effectiveness.\u003c/p\u003e","manuscriptTitle":"Determinants of willingness to use medical visit companion services among older adults in China: A Shapley value approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 20:53:48","doi":"10.21203/rs.3.rs-7211539/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T18:51:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T11:12:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T15:44:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86836406073371046400028379348755247602","date":"2025-11-03T18:37:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186720813686692275130182114244697173650","date":"2025-11-03T13:06:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T09:36:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174803041100126854112393932329206237900","date":"2025-10-20T03:53:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-18T01:49:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-07T19:18:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T17:35:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-06T12:39:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-08-06T12:35:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9db919ad-b15b-4c5a-a2fd-24926b5d5bc6","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T03:53:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-26 20:53:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7211539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7211539","identity":"rs-7211539","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}