Current status and influencing factors of decision-making readiness among lung cancer patients during chemotherapy: a cross-sectional study based on CSM model

Current status and influencing factors of decision-making readiness among lung cancer patients during chemotherapy: a cross-sectional study based on CSM model | 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 Current status and influencing factors of decision-making readiness among lung cancer patients during chemotherapy: a cross-sectional study based on CSM model Yan Wang, Jinping Li, Yumeng Quan, Minfeng Zhai, Xuenan Hu, Panrong Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7403827/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aimed to investigate the current status of decision - making readiness (DMR) among lung cancer patients during chemotherapy and to explore its influencing factors, with the goal of informing targeted clinical interventions. Methods A cross-sectional survey was conducted among 461 lung cancer patients during chemotherapy at a tertiary oncology hospital in China. Data were collected using the Decision Readiness Scale (C-PrepDM), Decisional Conflict Scale (DCS), and the Patient Participation in Treatment Decision-Making Attitude Questionnaire, along with demographic and clinical information. Hierarchical multiple regression analysis was performed to identify significant predictors of DMR. Results The overall level of DMR was moderate among the participants. Hierarchical regression revealed that economic status, place of residence, decisional conflict, and decision participation attitude were significant predictors (P < 0.05). Patients with higher income and those residing in rural or township areas showed better DMR. A positive attitude toward participation in decision-making was positively correlated with DMR (r = 0.382, P < 0.001), while decisional conflict was negatively correlated (r=–0.164, P < 0.001). Conclusion DMR in lung cancer patients during chemotherapy is influenced by both socioeconomic and psychosocial factors. Enhancing patient education, promoting active decision participation, and reducing decisional conflict through tailored communication strategies may strengthen patients’ readiness to engage in their treatment planning. Future multi-center and longitudinal studies are needed to further validate these findings. influencing factors decision - making readiness lung cancer CSM model chemotherapy Background Lung cancer is the most commonly diagnosed malignancy worldwide, with China reporting over 1 million new cases and 740,000 deaths in 2022, making it the leading cause of cancer-related morbidity and mortality in the country[1–2]. Chemotherapy remains a cornerstone in lung cancer management - used not only in advanced or metastatic stages, but also as neoadjuvant and adjuvant therapy[3]. Treatment decision-making, therefore, occurs repeatedly across the disease trajectory and is not limited to a single clinical point[4]. While treatment guidelines provide clinical direction, patients often face complex personal considerations such as potential side effects, functional limitations, emotional burden, and financial costs[5]. These factors influence patients’ willingness and readiness to initiate, continue, or adjust chemotherapy. Research shows that lung cancer patients may experience greater decisional complexity and lower decision-making readiness (DMR) than patients with other cancer types, due in part to rapid disease progression, high emotional distress at diagnosis, and limited time to process information[6–7]. A traditionally paternalistic approach in thoracic oncology may further hinder shared decision-making[8]. Inadequate decision preparation has been linked to higher decisional conflict, lower satisfaction, and poor treatment adherence[9]. In response, involving patients in treatment decisions has become a global priority. In China, national policies have called for enhanced patient engagement, yet many patients still struggle with low DMR due to insufficient support, limited health literacy, or unclear role perceptions[10]. DMR refers to a patient's cognitive and emotional preparedness to make informed choices throughout the treatment journey. Higher readiness has been associated with more effective risk-benefit evaluation, reduced decisional regret, and improved adherence[11]. This study adopts the Common Sense Model of Self-Regulation (CSM) to explore the psychological mechanisms underlying DMR in lung cancer patients[12]. According to CSM, individuals construct illness representations (cognitive stage), engage in coping strategies (coping stage), and adjust behavior based on outcomes (feedback stage). In this study, we focus on the first two stages: the cognitive stage is operationalized through three variables: decision-making attitude, decisional conflict, and decision regret; and the coping stage is represented by DMR in the context of chemotherapy. This study aims to assess DMR among patients undergoing chemotherapy for lung cancer and to examine how psychological factors - including decision-making attitude, decisional conflict, and decision regret - influence DMR within the CSM framework. We hypothesize that decision-making attitude, decisional conflict, and decision regret are significant predictors of DMR among lung cancer patients. Methods 2.1 Design, sample and setting A cross-sectional descriptive study was conducted using convenience sampling. Participants were recruited from hospitalized lung cancer patients undergoing chemotherapy at a specialized oncology hospital in Beijing between August and October 2024. The inclusion criteria were: (1) pathologically confirmed diagnosis of lung cancer; (2) age ≥ 18 years; (3) able to understand the content of the questionnaire as judged by a clinical nurse; (4) provided informed consent and voluntarily agreed to participate. The exclusion criteria were: (1) Critically ill patients who had discontinued chemotherapy; (2) patients with a documented history of psychiatric or severe cognitive disorders; (3) patients who were unaware of their diagnosis and whose medical decisions were made entirely by family members. The sample size was determined using Kendall’s method, which recommends 5 to 10 participants per variable in multivariate analysis[13]. With 25 variables in this study, the estimated sample size ranged from 125 to 250. Accounting for an anticipated 20% rate of invalid responses, the adjusted minimum sample size was calculated to be 157–313. To ensure statistical power and reduce sampling bias, a total of 461 patients were ultimately recruited, exceeding the required minimum. 2.2 Measures A structured questionnaire was developed based on findings from previous studies and the clinical characteristics of patients with lung cancer. To ensure content relevance and clarity, the research team held expert discussions, conducted a pilot survey, and made iterative revisions. The final version of the questionnaire was tailored to reflect the treatment decision-making context of lung cancer patients and consisted of following parts. Demographic Questionnaire The demographic information includes gender, age, educational level, marital status, and economic status. And treatment-related information covers pathological type, chemotherapy regimen, treatment cycle, etc. Chinese Version of the Preparation for Decision Making Scale (C-Prep DM) Originally developed by Canadian nursing scholars and later revised by Bennett et al.[14], the Decision Readiness Scale was translated into Chinese by Li Yu[15]. This instrument is designed to assess patients’ health-related DMR and to evaluate the effectiveness of decision-support interventions. The scale consists of 10 items rated on a 5-point Likert scale (1 = “not at all” to 5 = “a great deal”), with higher scores indicating greater DMR. The Cronbach’s ɑ coefficient for the scale was 0.946[15]. Patient Attitude Toward Treatment Decision-Making Scale Designed by Finnish scholar Sainio et al.[14] and translated into Chinese by Ma Lili in 2004[16], this scale consists of 12 items scored on a 3-point scale: very important (3 points), somewhat important (2 points), and not important (1 point). Higher scores indicate a more positive decision participant attitude (DPA) toward participation in treatment decisions. The Cronbach’s ɑ coefficient is 0.8506[16]. Decisional Conflict Scale (DCS) Developed by O’Connor et al.[17] and translated into Chinese by Li Yu[15]. It includes 16 items across three dimensions: information and values clarity, decision support and effectiveness, and decisional uncertainty. Items are rated on a 5-point Likert scale and converted to a total score from 0 to 100. Higher scores indicate higher decisional conflict, with scores above 25 indicating the presence of conflict, and scores above 37.5 suggesting possible decision delay. The scale’s Cronbach’s ɑ coefficient is 0.897[15]. Decision Regret Scale (DRS) Developed by Brehaut et al.[17] and translated into Chinese by Chen et al.[18]. The scale consists of 5 items rated on a 5-point Likert scale ranging from “strongly agree” (1 point) to “strongly disagree” (5 points), with items 2 and 4 reverse scored. The total score is calculated using the formula: (mean score of all items − 1) × 5, yielding a range from 0 to 20. Higher scores indicate greater regret related to the treatment decision. In this study, the Cronbach’s ɑ coefficient was 0.826[18]. 2.3 Data collection Data were collected using electronic questionnaires. Prior to the study, the project leader provided standardized training to designated nurses responsible for data collection, ensuring consistent instructions for questionnaire administration. Lung cancer patients undergoing chemotherapy were informed about the study’s purpose, significance, methodology, and confidentiality measures, and provided informed consent. To ensure data quality, each electronic device was permitted to submit the questionnaire only once. Clear instructions were provided for each section of the questionnaire, with an average completion time of approximately 15 minutes. If participants encountered any difficulties, they were instructed to promptly contact the trained nurses for assistance. These procedures ensured the standardization, completeness, and reliability of the collected data. 2.4 Statistical analysis Statistical analyses were conducted using R software (version 4.3.0). Measurement data with a normal distribution were expressed as mean ± standard deviation (SD), while non-normally distributed data were presented as median and interquartile range [M(P25, P75)]. For comparisons between two groups, independent samples t-tests were used for normally distributed data, and the Wilcoxon rank-sum test was applied for non-normally distributed data. For comparisons among three or more groups, one-way analysis of variance (ANOVA) was used for normally distributed data, and the Kruskal - Wallis test was used for non-normally distributed data.Correlation analyses were performed using Pearson or Spearman correlation coefficients, depending on the distribution and type of the data. Hierarchical multiple regression analysis was conducted to identify factors associated with DMR. A two-sided p-value of < 0.05 was considered statistically significant. Results 3.1 General information and univariate analysis of DMR among lung cancer patients during chemotherapy A total of 461 questionnaires were collected, of which 452 were valid, resulting in an effective response rate of 98.05%. Among the participants, 325 (71.9%) were male and 127 (28.1%) were female. A total of 161 patients (35.6%) were younger than 60 years, while 291 (64.4%) were aged 60 or above. Regarding marital status, 5 (1.1%) were unmarried, 365 (80.8%) were married, and 82 (18.1%) were divorced or widowed. Detailed demographic and clinical characteristics were presented in Table 1. The median decision readiness score among lung cancer patients during chemotherapy was 62.00 (56.00, 70.00), indicating a moderate level of preparedness. Univariate analysis revealed statistically significant differences in DMR across age groups, residential locations, and types of medication used (P < 0.05). Detailed results are presented in Table 1. Table 1 General information and univariate analysis of DMR among lung cancer patients during chemotherapy (n = 452) Variables Categories n (%) DMR [M(P25,P75)] Statistic P -value Total 452 (100) 62.00 (56.00, 70.00) Gender Z =-0.00 0.997 Men 325 (71.90) 62.00 (56.00, 70.00) Women 127 (28.10) 62.00 (56.00, 72.00) Age(years) Z =-2.24 0.025 18–59 161 (35.62) 64.00 (58.00, 72.00) ≥ 60 291 (64.38) 62.00 (56.00, 70.00) Marital status χ² =1.32# 0.516 Unmarried 5 (1.11) 62.00 (58.00, 84.00) Married 365 (80.75) 62.00 (56.00, 72.00) Divorce/Widow 82 (18.14) 62.00 (56.00, 68.00) Ethnicity Z =-0.52 0.600 Han ethnicity 420 (92.92) 62.00 (56.00, 70.00) ethnic minorities 32 (7.08) 64.00 (56.00, 73.00) Education level χ² =3.55# 0.170 Primary school 53 (11.73) 62.00 (56.00, 68.00) Secondary school/ vocational school 260 (57.52) 64.00 (56.00, 74.00) college degree or above 139 (30.75) 62.00 (56.00, 70.00) Primary caregiver χ² =0.50# 0.778 Spouse 267 (59.07) 62.00 (56.00, 71.00) Children 116 (25.66) 62.00 (58.00, 70.50) Alone/Others 69 (15.27) 62.00 (56.00, 70.00) Economic status χ² =5.75# 0.056 6000 100 (22.12) 64.00 (58.00, 76.00) Occupation χ² =4.84# 0.184 Retired 147 (32.52) 62.00 (54.00, 70.00) Employees of enterprises/ institutions 98 (21.68) 64.00 (58.00, 71.50) Farmer 97 (21.46) 62.00 (56.00, 70.00) Unemployed / Self-employed 110 (24.34) 64.00 (58.00, 71.50) Residence χ² =8.65# 0.013 City 251 (55.53) 62.00 (56.00, 68.00) Country side 42 (9.29) 65.00 (58.00, 78.50) Town 159 (35.18) 64.00 (58.00, 74.00) Medical insurance χ² =2.72# 0.257 New Rural Cooperative Medical Scheme 112 (24.78) 62.00 (56.00, 70.00) Urban Employee/ Resident Basic Medical Insurance 320 (70.80) 62.00 (56.00, 70.50) Others 20 (4.42) 66.00 (60.00, 73.00) Treatment cycles χ² =1.37# 0.505 First cycle 66 (14.60) 62.00 (56.50, 67.50) 2–4 cycles 166 (36.73) 64.00 (56.50, 72.00) 5 or more cycles 220 (48.67) 62.00 (56.00, 70.00) Pathological type Z =-0.75 0.453 Non-small cell lung cancer 293 (64.82) 62.00 (56.00, 72.00) Small cell lung cancer 159 (35.18) 62.00 (56.00, 68.00) Treatment regimen Z =-0.38 0.707 Monotherapy 123 (27.21) 62.00 (56.00, 70.00) Combination therapy 329 (72.79) 62.00 (56.00, 72.00) Cancer stage χ² =1.44# 0.695 I 23 (5.09) 64.00 (60.00, 70.00) II 32 (7.08) 64.00 (58.00, 68.50) III 124 (27.43) 62.00 (58.00, 72.00) IV 273 (60.40) 62.00 (56.00, 70.00) Line of therapy χ² =1.06# 0.589 First-line 307 (67.92) 62.00 (56.00, 70.00) Second- to third-line 118 (26.11) 62.00 (56.00, 72.00) Fourth-line or above 27 (5.97) 60.00 (53.00, 67.00) History of lung surgery Z =-1.08 0.279 No 292 (64.60) 62.00 (56.00, 72.00) Yes 160 (35.40) 62.00 (55.50, 70.00) Number of medications taken χ² =11.37# 0.003 None 61 (13.50) 64.00 (58.00, 80.00) 1–2 types 273 (60.40) 62.00 (54.00, 68.00) 3 or more types 118 (26.11) 64.00 (58.50, 70.00) M: Median, Q₁: 1st Quartile, Q₃: 3rd Quartile, #: Kruskal-waills test 3.2 Scores of scales among lung cancer patients during chemotherapy The median total score of the DMR among 452 lung cancer patients undergoing chemotherapy was 62.00 (56.00, 70.00). The DPA had a median score of 29.00 (26.00, 32.00), the DCS score was 32.00 (23.25, 36.00), and the DRS score was 9.00 (8.00, 11.00). Detailed descriptive statistics were presented in Table 2. Table 2 Scores of scales for lung cancer patients during chemotherapy Dimension Number of items Score range Total score Average item score DMR 10 20ཞ100 62.00(56.00, 70.00) 6.20(5.60, 7.00) DPA 12 12ཞ36 29.00(26.00, 32.00) 5.90(4.86, 6.94) DCS 16 0ཞ100 32.00(23.25, 36.00) 2.00(1.45, 2.25) DRS 5 0ཞ20 9.00(8.00, 11.00) 1.80(1.60, 2.20) 3.3 Correlation analysis of DMR among lung cancer patients during chemotherapy Spearman correlation analysis indicated that DMR was positively correlated with DPA (r = 0.382, P < 0.001), and negatively correlated with the total score and all subdimensions of the DCS (r = − 0.212 to − 0.164, P < 0.001). Detailed results were shown in Table 3. Table 3 Correlation between DMR and DCS, DPA, and DRS in lung cancer patients during chemotherapy Dimension Total Score of DPA Information and Values Clarity Decision Support and Effectiveness Decisional Uncertainty Total Score of DCS Total Score of DRS Total Score of DMR 0.382** -0.197** -0.212** -0.185** -0.164** 0.005 ** P <0.001 3.4 Multivariate Analysis of DMR among lung cancer patients during chemotherapy The total DMR score was used as the dependent variable. Independent variables included those identified as statistically significant in univariate and correlation analyses, as well as clinically relevant variables that, although not statistically significant (P > 0.05), were theoretically supported by existing literature and considered potentially influential. A multivariate hierarchical linear regression analysis was performed. For unordered categorical variables, dummy variables were created. The coding of variables is presented in Table 4. In the hierarchical regression model, demographic characteristics and types of medication were entered in the first block, while scores for decisional conflict and decision participation attitude were entered in the second block. The analysis revealed that economic status, place of residence, decisional conflict, and decision participation attitude were significant predictors of decision readiness among lung cancer patients undergoing chemotherapy (P < 0.05), as detailed in Table 5. Table 4 Variable assignments of factors Variable Coding Method Age(years) 18 ~ 59 = 1, ≥ 60 = 2 Economic status Dummy variables were created using "< 4000 RMB" as the reference group. Place of residence Dummy variables were created using "urban areas" as the reference group. Number of medications used Dummy variables were created using "none" as the reference group. DCS Entered as a continuous variable DPA Entered as a continuous variable Table 5 Hierarchical analysis of DMR among lung cancer patients during chemotherapy (n = 452) Model Variable B SE β t P 95%CI 1 (Constant) 32.618 1.099 29.676 < .001 30.458 ~ 34.778 Age -1.117 0.624 -0.081 -1.792 0.074 -2.343 ~ 0.108 Place of residence (Ref = Urban) Country side 4.046 1.132 0.178 3.575 <0.001 1.822 ~ 6.27 Town 2.531 0.69 0.184 3.671 <0.001 1.176 ~ 3.887 Economic status (Ref = 6000 RMB 3.387 0.864 0.214 3.921 <0.001 1.689 ~ 5.085 Medication type (Ref = None) 1–2 types -3.056 0.903 -0.227 -3.384 0.001 -4.830~-1.281 ≥ 3 types -2.365 1.003 -0.158 -2.359 0.019 -4.335~-0.395 2 (Constant) 24.739 2.623 9.432 <0.001 19.585 ~ 29.894 Age -0.727 0.548 -0.053 -1.325 0.186 -1.804 ~ 0.351 Place of residence (Ref = Urban) Country side 3.119 0.991 0.138 3.147 0.002 1.172 ~ 5.067 Town 2.193 0.603 0.159 3.636 <0.001 1.008 ~ 3.378 Economic status (Ref = 6000 RMB 1.766 0.768 0.111 2.300 0.022 0.257 ~ 3.274 Medication type (Ref = None) 1–2 types -0.299 0.822 -0.022 -0.364 0.716 -1.914 ~ 1.316 ≥ 3 types 0.817 0.921 0.054 0.887 0.376 -0.994 ~ 2.628 DPA 0.390 0.069 0.250 5.672 <0.001 0.255 ~ 0.526 DCS -0.193 0.025 -0.347 -7.618 <0.001 -0.242 ~ 0.143 In the first step, R²=0.303, ΔR²=0.092, P < 0.001;In the second step, R²= 0.558, ΔR²=0.219, P < 0.001. Discussion 4.1 Status of DMR among lung cancer patients during chemotherapy This study found that lung cancer patients during chemotherapy had a moderate level of decision-making readiness, consistent with the findings of Zhang et al. [10]. However, compared with Tan et al. [19], who studied younger female patients, the scores in this study were lower. This may be due to differences in age, health literacy, and decision-making behaviors, as most participants in this study were older adults. According to the CSM, older patients may have lower decision readiness due to limited illness perception or confidence in treatment benefits. In addition, the scales used in the two studies differ. Tan et al. used the PrepDM scale, which emphasizes communication and value weighing, while this study used the C-PrepDM, focusing more on information needs and risk communication. In light of these findings, it is recommended that nurses enhance education using visual tools (e.g., brochures, videos) to simplify information and strengthen treatment beliefs. Regular assessment of DMR throughout chemotherapy and targeted education based on patient needs are recommended. Encouraging shared decision-making among healthcare providers, patients, and families can also help patients take a more active role in treatment decisions. 4.2 DMR in lung cancer patients during chemotherapy was influenced by various demographic and psychosocial factors 4.2.1 Economic status and place of residence significantly affect DMR The study found that economic status and place of residence significantly influenced DMR in lung cancer patients during chemotherapy. Patients with a monthly income of ≥ 4000 RMB had significantly higher decision readiness than those with lower income (P < 0.05), consistent with Lu’s findings[20]. This supports the social determinants of health theory[21–22], which suggests that disparities in resources and social standing affect individuals’ ability to access healthcare information and services indirectly influencing their decision-making ability. Patients with better financial conditions may communicate more effectively with healthcare providers and are more likely to seek and understand medical information. Place of residence also played an important role. Patients from rural and township areas showed higher decision readiness than those in urban areas (P < 0.001). In rural settings, family members are often more involved in healthcare decisions, and close community ties promote shared decision-making. These patients may view participating in medical decisions as a responsibility rather than a choice, often relying on doctors and family for guidance, which may enhance their readiness to engage in treatment decisions. 4.2.2 Higher DPA is associated with greater DMR This study found a significant positive correlation between DPA and DMR in lung cancer patients during chemotherapy (r = 0.382, P < 0.001), suggesting that patients with a more positive attitude toward participating in decision-making are more prepared to make treatment decisions. This aligns with the findings of Ma[16], who reported that cancer patients with high DMR are more actively involved in care decisions. The key factor is patients’ ability to understand medical information and recognize their role in managing their health. According to the CSM, patients’ knowledge about chemotherapy directly influences their willingness to participate, which shapes their decision-making behavior[11]. When patients receive clear, accurate information - such as treatment plans and strategies for managing side effects - their motivation to engage increases. This often leads to active behaviors like asking questions and sharing treatment preferences. To support this, healthcare providers should establish regular assessments of DMR and adjust education or support strategies as needed. This can improve shared decision-making between providers, patients, and families, and help ensure better treatment engagement during chemotherapy. 4.2.3 Lower levels of decision conflict are associated with higher DMR This study found a negative correlation between decisional conflict and DMR among lung cancer patients during chemotherapy (r = -0.164, P < 0.001), indicating that patients with less conflict tend to be more prepared to make treatment decisions. This supports the findings of Sui [23], who observed that patients with high DMR often communicate better with healthcare providers and can align medical information with their personal needs, leading to improved treatment outcomes. Although the cross-sectional design limits causal interpretation, the CSM suggests a possible two-way relationship: high decisional conflict may reduce readiness by increasing uncertainty, while higher readiness may lower conflict by improving information processing. Future longitudinal research is needed to explore this relationship further. Clinically, this association highlights the importance of reducing decisional conflict to improve readiness. Nurses can support patients by providing clear information, strengthening communication, and helping clarify expectations. For those with high conflict, targeted strategies - such as addressing cost concerns or fears about side effects - can be developed. Involving family members in these interventions may also help improve DMR, treatment adherence, and overall quality of life. 4.3 Study Limitation this study is limited by its single-center, cross-sectional design. Future research should involve multi-center, interdisciplinary studies to develop strategies that reduce decisional conflict and enhance participation attitudes, thereby further improving decision readiness and patient outcomes. 4.4 Clinical Implications This study highlights the moderate level of DMR among lung cancer patients during chemotherapy and identifies economic status, place of residence, decisional conflict, and decision participation attitude as significant influencing factors. These findings suggest several implications for psycho-oncology practice: Targeted Decision Support Interventions : Clinicians should integrate structured decision support interventions tailored to patients’ socioeconomic background. Patients with lower income or limited access to resources may benefit from simplified educational materials and individualized counseling to improve health literacy and engagement. Strengthening Communication in Shared Decision-Making (SDM) : Given the strong association between decision participation attitude and readiness, oncology nurses and psychosocial professionals should foster a participatory environment that encourages patients to express their preferences and concerns, especially during key chemotherapy decision points. Addressing Decisional Conflict through Psychoeducation : The negative correlation between decisional conflict and DMR suggests the need for early screening and psychological support for patients exhibiting indecision or ambivalence. Tools such as DCS can be used in clinical settings to identify high-risk patients and trigger early psycho-oncological interventions. Cultural and Contextual Sensitivity : The finding that rural patients demonstrated higher DMR than urban counterparts underscores the importance of understanding sociocultural dynamics in decision-making. Clinical teams should engage family members and respect local values, especially in collectivist settings, to enhance patient participation. Routine DMR Monitoring and Adaptive Care Plans : DMR is a dynamic process that may fluctuate throughout the chemotherapy course. Incorporating regular DMR assessments into clinical workflows allows timely adjustment of supportive care strategies, ensuring that patients remain engaged and confident in their treatment journey. By embedding these strategies into clinical pathways, oncology teams can empower patients, reduce psychological distress related to decision-making, and ultimately promote better treatment adherence and quality of life for individuals facing complex cancer care decisions. Conclusion In summary, this study found that lung cancer patients during chemotherapy have a moderate level of DMR, influenced by factors such as economic status, place of residence, decisional conflict, and DPA. During treatment, patients face not only physical and emotional burdens but also uncertainties about treatment outcomes and pressure from family and social contexts. To improve decision readiness and treatment adherence, healthcare providers should implement targeted interventions - including health education, psychological support, and enhanced clinical monitoring - to ultimately improve patients’ quality of life. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publish The authors affirm that human research participants provided informed consent for publication of the data in all tables. Author Contribution Conceptualization: Yan Wang, Jinping Li, Xin Sun. Data curation: Yan Wang, Xin Sun, Jinping Li, Yumeng Quan. Formal analysis: Jinping Li, Yumeng Quan. Investigation: Jinping Li, Minfeng Zhai, Xuenan Hu, Panrong Wang. Methodology: Xin Sun, Yan Wang, Jinping Li, Yumeng Quan, Panrong Wang. Visualization: Xin Sun, Yan Wang. Writing - original draft: Yan Wang. Writing - review & editing: Yan Wang, Jinping Li, Yumeng Quan. Funding acquisition: Yan Wang. Project administration: Yan Wang & Xin Sun. Supervision: Xin Sun. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, & Jemal A (2022) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 229–263 Han B, Zheng R, Zeng H, et al (2024) Cancer incidence and mortality in China, 2022. 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Journal of Nursing 25(7):42–44 Tan TN, Han J, Fan TT, et al (2023) Latent Class Analysis of Decision-Making Readiness in Young and Middle-Aged Female Cancer Patients [J]. Military Nursing 40(6):25–29 Lu SL (2022) Factors Influencing and Experiences of Cancer Patients' Participation in Treatment Decision-Making [D]. Jinan: Shandong University Song XP, Xing TH, Ji JL, et al (2025) Current Status and Influencing Factors of Decision-Making Readiness in HIV/AIDS Patients Undergoing Antiviral Therapy [J]. Dermatology and Venereology 47(1):26–29 Liu Chang (2022) Construction and Application of a Risk Prediction and Assessment Index System for Disability in the Elderly Based on the Theory of Social Determinants of Health [D]. Tangshan: North China University of Science and Technology Sui QY (2021) Current Status and Influencing Factors of Decision-Making Participation and Decision Regret in Breast Cancer Patients [D]. Jinan: Shandong University Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7403827","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528169024,"identity":"2c958bee-4e70-447a-b6b6-9818056b8da7","order_by":0,"name":"Yan Wang","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wang","suffix":""},{"id":528169025,"identity":"5eeb78bb-ea77-45c3-ba05-ef559cda76a7","order_by":1,"name":"Jinping Li","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jinping","middleName":"","lastName":"Li","suffix":""},{"id":528169026,"identity":"fc7ebd09-ece3-49f1-bc4e-8096ca3f8475","order_by":2,"name":"Yumeng Quan","email":"","orcid":"","institution":"Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yumeng","middleName":"","lastName":"Quan","suffix":""},{"id":528169027,"identity":"7882353e-e532-4d45-b891-ad987ef3a975","order_by":3,"name":"Minfeng Zhai","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Minfeng","middleName":"","lastName":"Zhai","suffix":""},{"id":528169028,"identity":"2a3c9043-46f7-4716-a6b5-70e61109e6a0","order_by":4,"name":"Xuenan Hu","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xuenan","middleName":"","lastName":"Hu","suffix":""},{"id":528169029,"identity":"5b5c02a1-c05f-4bae-a18f-7ff0713d1b58","order_by":5,"name":"Panrong Wang","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Panrong","middleName":"","lastName":"Wang","suffix":""},{"id":528169030,"identity":"7331c526-cbcd-4be9-be94-868fcf3a780b","order_by":6,"name":"Xin Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYLCCBAYbHn75xwcOfPhBvJY0OcmGtMSDM3uIt+ewscGBHOPDHGxEqJWfkXtM4uGOw4kbDpz5cJiBh0GeX+wAfi0GN/KSDRLPpCfOPNi74XCBBYPhzNkJBLRI5Bg+SGyzTuw7zLvh8AwehgSD2wS0yM/IMTiQ2Mac2HCM58FhHjYitDDcANvibCxwhoeBOC0GZ94YGyS2AQN5BpsBMJAlCPtFvj3HTPJnGzAqJZgff/jww0aeX5qQw9CABGnKR8EoGAWjYBRgBwA9CkiBNe4E8AAAAABJRU5ErkJggg==","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-08-19 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07:38:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1466560,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7403827/v1/2c39ed1f-a360-48b3-87d1-9bdf010f5102.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Current status and influencing factors of decision-making readiness among lung cancer patients during chemotherapy: a cross-sectional study based on CSM model","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer is the most commonly diagnosed malignancy worldwide, with China reporting over 1 million new cases and 740,000 deaths in 2022, making it the leading cause of cancer-related morbidity and mortality in the country[1\u0026ndash;2]. Chemotherapy remains a cornerstone in lung cancer management - used not only in advanced or metastatic stages, but also as neoadjuvant and adjuvant therapy[3]. Treatment decision-making, therefore, occurs repeatedly across the disease trajectory and is not limited to a single clinical point[4].\u003c/p\u003e\n\u003cp\u003eWhile treatment guidelines provide clinical direction, patients often face complex personal considerations such as potential side effects, functional limitations, emotional burden, and financial costs[5]. These factors influence patients\u0026rsquo; willingness and readiness to initiate, continue, or adjust chemotherapy. Research shows that lung cancer patients may experience greater decisional complexity and lower decision-making readiness (DMR) than patients with other cancer types, due in part to rapid disease progression, high emotional distress at diagnosis, and limited time to process information[6\u0026ndash;7]. A traditionally paternalistic approach in thoracic oncology may further hinder shared decision-making[8]. Inadequate decision preparation has been linked to higher decisional conflict, lower satisfaction, and poor treatment adherence[9].\u003c/p\u003e\n\u003cp\u003eIn response, involving patients in treatment decisions has become a global priority. In China, national policies have called for enhanced patient engagement, yet many patients still struggle with low DMR due to insufficient support, limited health literacy, or unclear role perceptions[10].\u003c/p\u003e\n\u003cp\u003eDMR refers to a patient\u0026apos;s cognitive and emotional preparedness to make informed choices throughout the treatment journey. Higher readiness has been associated with more effective risk-benefit evaluation, reduced decisional regret, and improved adherence[11]. This study adopts the Common Sense Model of Self-Regulation (CSM) to explore the psychological mechanisms underlying DMR in lung cancer patients[12]. According to CSM, individuals construct illness representations (cognitive stage), engage in coping strategies (coping stage), and adjust behavior based on outcomes (feedback stage). In this study, we focus on the first two stages: the cognitive stage is operationalized through three variables: decision-making attitude, decisional conflict, and decision regret; and the coping stage is represented by DMR in the context of chemotherapy.\u003c/p\u003e\n\u003cp\u003eThis study aims to assess DMR among patients undergoing chemotherapy for lung cancer and to examine how psychological factors - including decision-making attitude, decisional conflict, and decision regret - influence DMR within the CSM framework. We hypothesize that decision-making attitude, decisional conflict, and decision regret are significant predictors of DMR among lung cancer patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Design, sample and setting\u003c/h2\u003e\n \u003cp\u003eA cross-sectional descriptive study was conducted using convenience sampling. Participants were recruited from hospitalized lung cancer patients undergoing chemotherapy at a specialized oncology hospital in Beijing between August and October 2024. The inclusion criteria were: (1) pathologically confirmed diagnosis of lung cancer; (2) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (3) able to understand the content of the questionnaire as judged by a clinical nurse; (4) provided informed consent and voluntarily agreed to participate. The exclusion criteria were: (1) Critically ill patients who had discontinued chemotherapy; (2) patients with a documented history of psychiatric or severe cognitive disorders; (3) patients who were unaware of their diagnosis and whose medical decisions were made entirely by family members.\u003c/p\u003e\n \u003cp\u003eThe sample size was determined using Kendall\u0026rsquo;s method, which recommends 5 to 10 participants per variable in multivariate analysis[13]. With 25 variables in this study, the estimated sample size ranged from 125 to 250. Accounting for an anticipated 20% rate of invalid responses, the adjusted minimum sample size was calculated to be 157\u0026ndash;313. To ensure statistical power and reduce sampling bias, a total of 461 patients were ultimately recruited, exceeding the required minimum.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Measures\u003c/h2\u003e\n \u003cp\u003eA structured questionnaire was developed based on findings from previous studies and the clinical characteristics of patients with lung cancer. To ensure content relevance and clarity, the research team held expert discussions, conducted a pilot survey, and made iterative revisions. The final version of the questionnaire was tailored to reflect the treatment decision-making context of lung cancer patients and consisted of following parts.\u003c/p\u003e\n \u003cp\u003eDemographic Questionnaire\u003c/p\u003e\n \u003cp\u003eThe demographic information includes gender, age, educational level, marital status, and economic status. And treatment-related information covers pathological type, chemotherapy regimen, treatment cycle, etc.\u003c/p\u003e\n \u003cp\u003eChinese Version of the Preparation for Decision Making Scale (C-Prep DM)\u003c/p\u003e\n \u003cp\u003eOriginally developed by Canadian nursing scholars and later revised by Bennett et al.[14], the Decision Readiness Scale was translated into Chinese by Li Yu[15]. This instrument is designed to assess patients\u0026rsquo; health-related DMR and to evaluate the effectiveness of decision-support interventions. The scale consists of 10 items rated on a 5-point Likert scale (1 = \u0026ldquo;not at all\u0026rdquo; to 5 = \u0026ldquo;a great deal\u0026rdquo;), with higher scores indicating greater DMR. The Cronbach\u0026rsquo;s ɑ coefficient for the scale was 0.946[15].\u003c/p\u003e\n \u003cp\u003ePatient Attitude Toward Treatment Decision-Making Scale\u003c/p\u003e\n \u003cp\u003eDesigned by Finnish scholar Sainio et al.[14] and translated into Chinese by Ma Lili in 2004[16], this scale consists of 12 items scored on a 3-point scale: very important (3 points), somewhat important (2 points), and not important (1 point). Higher scores indicate a more positive decision participant attitude (DPA) toward participation in treatment decisions. The Cronbach\u0026rsquo;s ɑ coefficient is 0.8506[16].\u003c/p\u003e\n \u003cp\u003eDecisional Conflict Scale (DCS)\u003c/p\u003e\n \u003cp\u003eDeveloped by O\u0026rsquo;Connor et al.[17] and translated into Chinese by Li Yu[15]. It includes 16 items across three dimensions: information and values clarity, decision support and effectiveness, and decisional uncertainty. Items are rated on a 5-point Likert scale and converted to a total score from 0 to 100. Higher scores indicate higher decisional conflict, with scores above 25 indicating the presence of conflict, and scores above 37.5 suggesting possible decision delay. The scale\u0026rsquo;s Cronbach\u0026rsquo;s ɑ coefficient is 0.897[15].\u003c/p\u003e\n \u003cp\u003eDecision Regret Scale (DRS)\u003c/p\u003e\n \u003cp\u003eDeveloped by Brehaut et al.[17] and translated into Chinese by Chen et al.[18]. The scale consists of 5 items rated on a 5-point Likert scale ranging from \u0026ldquo;strongly agree\u0026rdquo; (1 point) to \u0026ldquo;strongly disagree\u0026rdquo; (5 points), with items 2 and 4 reverse scored. The total score is calculated using the formula: (mean score of all items \u0026minus;\u0026thinsp;1) \u0026times; 5, yielding a range from 0 to 20. Higher scores indicate greater regret related to the treatment decision. In this study, the Cronbach\u0026rsquo;s ɑ coefficient was 0.826[18].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Data collection\u003c/h2\u003e\n \u003cp\u003eData were collected using electronic questionnaires. Prior to the study, the project leader provided standardized training to designated nurses responsible for data collection, ensuring consistent instructions for questionnaire administration. Lung cancer patients undergoing chemotherapy were informed about the study\u0026rsquo;s purpose, significance, methodology, and confidentiality measures, and provided informed consent.\u003c/p\u003e\n \u003cp\u003eTo ensure data quality, each electronic device was permitted to submit the questionnaire only once. Clear instructions were provided for each section of the questionnaire, with an average completion time of approximately 15 minutes. If participants encountered any difficulties, they were instructed to promptly contact the trained nurses for assistance. These procedures ensured the standardization, completeness, and reliability of the collected data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analyses were conducted using R software (version 4.3.0). Measurement data with a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while non-normally distributed data were presented as median and interquartile range [M(P25, P75)]. For comparisons between two groups, independent samples t-tests were used for normally distributed data, and the Wilcoxon rank-sum test was applied for non-normally distributed data. For comparisons among three or more groups, one-way analysis of variance (ANOVA) was used for normally distributed data, and the Kruskal - Wallis test was used for non-normally distributed data.Correlation analyses were performed using Pearson or Spearman correlation coefficients, depending on the distribution and type of the data. Hierarchical multiple regression analysis was conducted to identify factors associated with DMR. A two-sided p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.1 General information and univariate analysis of DMR among lung cancer patients during chemotherapy\u003c/h2\u003e\n \u003cp\u003eA total of 461 questionnaires were collected, of which 452 were valid, resulting in an effective response rate of 98.05%. Among the participants, 325 (71.9%) were male and 127 (28.1%) were female. A total of 161 patients (35.6%) were younger than 60 years, while 291 (64.4%) were aged 60 or above. Regarding marital status, 5 (1.1%) were unmarried, 365 (80.8%) were married, and 82 (18.1%) were divorced or widowed. Detailed demographic and clinical characteristics were presented in Table 1.\u003c/p\u003e\n \u003cp\u003eThe median decision readiness score among lung cancer patients during chemotherapy was 62.00 (56.00, 70.00), indicating a moderate level of preparedness. Univariate analysis revealed statistically significant differences in DMR across age groups, residential locations, and types of medication used (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Detailed results are presented in Table 1.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGeneral information and univariate analysis of DMR among lung cancer patients during chemotherapy (n\u0026thinsp;=\u0026thinsp;452)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDMR\u003c/p\u003e\n \u003cp\u003e[M(P25,P75)]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e452 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325 (71.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (28.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161 (35.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (58.00, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e291 (64.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=1.32#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (58.00, 84.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365 (80.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorce/Widow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (18.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHan ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e420 (92.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eethnic minorities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (7.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (56.00, 73.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=3.55#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (11.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary school/\u003c/p\u003e\n \u003cp\u003evocational school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260 (57.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (56.00, 74.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecollege degree or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (30.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary caregiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=0.50#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e267 (59.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 71.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChildren\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116 (25.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (58.00, 70.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlone/Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (15.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=5.75#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176 (38.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4000\u0026ndash;6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176 (38.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (22.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (58.00, 76.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=4.84#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147 (32.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (54.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployees of enterprises/\u003c/p\u003e\n \u003cp\u003einstitutions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98 (21.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (58.00, 71.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (21.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed / Self-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (24.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (58.00, 71.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=8.65#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251 (55.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCountry side\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (9.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.00 (58.00, 78.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159 (35.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (58.00, 74.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=2.72#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew Rural Cooperative Medical Scheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (24.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Employee/\u003c/p\u003e\n \u003cp\u003eResident Basic Medical Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320 (70.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (4.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.00 (60.00, 73.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreatment cycles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=1.37#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (14.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.50, 67.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;4 cycles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166 (36.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (56.50, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 or more cycles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220 (48.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-small cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e293 (64.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159 (35.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreatment regimen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123 (27.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCombination therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e329 (72.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=1.44#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (5.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (60.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (7.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (58.00, 68.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (27.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (58.00, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273 (60.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLine of therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=1.06#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e307 (67.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecond- to third-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118 (26.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFourth-line or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (5.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.00 (53.00, 67.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory of lung surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e=-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292 (64.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (56.00, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (35.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (55.50, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of medications taken\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=11.37#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (13.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (58.00, 80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2 types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273 (60.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 (54.00, 68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 or more types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118 (26.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.00 (58.50, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eM: Median, Q₁: 1st Quartile, Q₃: 3rd Quartile, #: Kruskal-waills test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.2 Scores of scales among lung cancer patients during chemotherapy\u003c/h2\u003e\n \u003cp\u003eThe median total score of the DMR among 452 lung cancer patients undergoing chemotherapy was 62.00 (56.00, 70.00). The DPA had a median score of 29.00 (26.00, 32.00), the DCS score was 32.00 (23.25, 36.00), and the DRS score was 9.00 (8.00, 11.00). Detailed descriptive statistics were presented in Table 2.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eScores of scales for lung cancer patients during chemotherapy\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDimension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of items\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScore range\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage item score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20ཞ100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00(56.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.20(5.60, 7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12ཞ36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.00(26.00, 32.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.90(4.86, 6.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0ཞ100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.00(23.25, 36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00(1.45, 2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0ཞ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.00(8.00, 11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80(1.60, 2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.3 Correlation analysis of DMR among lung cancer patients during chemotherapy\u003c/h2\u003e\n \u003cp\u003eSpearman correlation analysis indicated that DMR was positively correlated with DPA (r\u0026thinsp;=\u0026thinsp;0.382, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and negatively correlated with the total score and all subdimensions of the DCS (r = \u0026minus;\u0026thinsp;0.212 to \u0026minus;\u0026thinsp;0.164, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Detailed results were shown in Table 3.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCorrelation between DMR and DCS, DPA, and DRS in lung cancer patients during chemotherapy\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDimension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Score of DPA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInformation and Values Clarity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDecision Support and Effectiveness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDecisional Uncertainty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Score of DCS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Score of DRS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Score of DMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.382**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.197**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.212**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.185**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.164**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e**\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.4 Multivariate Analysis of DMR among lung cancer patients during chemotherapy\u003c/h2\u003e\n \u003cp\u003eThe total DMR score was used as the dependent variable. Independent variables included those identified as statistically significant in univariate and correlation analyses, as well as clinically relevant variables that, although not statistically significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), were theoretically supported by existing literature and considered potentially influential. A multivariate hierarchical linear regression analysis was performed. For unordered categorical variables, dummy variables were created. The coding of variables is presented in Table\u0026nbsp;4.\u003c/p\u003e\n \u003cp\u003eIn the hierarchical regression model, demographic characteristics and types of medication were entered in the first block, while scores for decisional conflict and decision participation attitude were entered in the second block. The analysis revealed that economic status, place of residence, decisional conflict, and decision participation attitude were significant predictors of decision readiness among lung cancer patients undergoing chemotherapy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as detailed in Table\u0026nbsp;5.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eVariable assignments of factors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoding Method\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026thinsp;~\u0026thinsp;59\u0026thinsp;=\u0026thinsp;1, \u0026ge;\u0026thinsp;60\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDummy variables were created using \u0026quot;\u0026lt; 4000 RMB\u0026quot; as the reference group.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDummy variables were created using \u0026quot;urban areas\u0026quot; as the reference group.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of medications used\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDummy variables were created using \u0026quot;none\u0026quot; as the reference group.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEntered as a continuous variable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEntered as a continuous variable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eHierarchical analysis of DMR among lung cancer patients during chemotherapy (n\u0026thinsp;=\u0026thinsp;452)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.458\u0026thinsp;~\u0026thinsp;34.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.343\u0026thinsp;~\u0026thinsp;0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlace of residence (Ref\u0026thinsp;=\u0026thinsp;Urban)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCountry side\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.822\u0026thinsp;~\u0026thinsp;6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.176\u0026thinsp;~\u0026thinsp;3.887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic status (Ref\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;4000 RMB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4000\u0026thinsp;~\u0026thinsp;6000 RMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.625\u0026thinsp;~\u0026thinsp;3.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;6000 RMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.689\u0026thinsp;~\u0026thinsp;5.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedication type (Ref\u0026thinsp;=\u0026thinsp;None)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2 types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.830~-1.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;3 types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.335~-0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.585\u0026thinsp;~\u0026thinsp;29.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.804\u0026thinsp;~\u0026thinsp;0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlace of residence (Ref\u0026thinsp;=\u0026thinsp;Urban)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCountry side\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.172\u0026thinsp;~\u0026thinsp;5.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.008\u0026thinsp;~\u0026thinsp;3.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic status (Ref\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;4000 RMB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4000\u0026thinsp;~\u0026thinsp;6000 RMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.319\u0026thinsp;~\u0026thinsp;2.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;6000 RMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.257\u0026thinsp;~\u0026thinsp;3.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedication type (Ref\u0026thinsp;=\u0026thinsp;None)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2 types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.914\u0026thinsp;~\u0026thinsp;1.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;3 types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.994\u0026thinsp;~\u0026thinsp;2.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.255\u0026thinsp;~\u0026thinsp;0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.242\u0026thinsp;~\u0026thinsp;0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the first step, R\u0026sup2;=0.303, \u0026Delta;R\u0026sup2;=0.092, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001;In the second step, R\u0026sup2;= 0.558, \u0026Delta;R\u0026sup2;=0.219, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e4.1 Status of DMR among lung cancer patients during chemotherapy\u003c/h2\u003e\n \u003cp\u003eThis study found that lung cancer patients during chemotherapy had a moderate level of decision-making readiness, consistent with the findings of Zhang et al. [10]. However, compared with Tan et al. [19], who studied younger female patients, the scores in this study were lower. This may be due to differences in age, health literacy, and decision-making behaviors, as most participants in this study were older adults. According to the CSM, older patients may have lower decision readiness due to limited illness perception or confidence in treatment benefits. In addition, the scales used in the two studies differ. Tan et al. used the PrepDM scale, which emphasizes communication and value weighing, while this study used the C-PrepDM, focusing more on information needs and risk communication.\u003c/p\u003e\n \u003cp\u003eIn light of these findings, it is recommended that nurses enhance education using visual tools (e.g., brochures, videos) to simplify information and strengthen treatment beliefs. Regular assessment of DMR throughout chemotherapy and targeted education based on patient needs are recommended. Encouraging shared decision-making among healthcare providers, patients, and families can also help patients take a more active role in treatment decisions.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.2 DMR in lung cancer patients during chemotherapy was influenced by various demographic and psychosocial factors\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e4.2.1 Economic status and place of residence significantly affect DMR\u003c/h2\u003e\n \u003cp\u003eThe study found that economic status and place of residence significantly influenced DMR in lung cancer patients during chemotherapy. Patients with a monthly income of \u0026ge;\u0026thinsp;4000 RMB had significantly higher decision readiness than those with lower income (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), consistent with Lu\u0026rsquo;s findings[20]. This supports the social determinants of health theory[21\u0026ndash;22], which suggests that disparities in resources and social standing affect individuals\u0026rsquo; ability to access healthcare information and services indirectly influencing their decision-making ability. Patients with better financial conditions may communicate more effectively with healthcare providers and are more likely to seek and understand medical information.\u003c/p\u003e\n \u003cp\u003ePlace of residence also played an important role. Patients from rural and township areas showed higher decision readiness than those in urban areas (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In rural settings, family members are often more involved in healthcare decisions, and close community ties promote shared decision-making. These patients may view participating in medical decisions as a responsibility rather than a choice, often relying on doctors and family for guidance, which may enhance their readiness to engage in treatment decisions.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e4.2.2 Higher DPA is associated with greater DMR\u003c/h2\u003e\n \u003cp\u003eThis study found a significant positive correlation between DPA and DMR in lung cancer patients during chemotherapy (r\u0026thinsp;=\u0026thinsp;0.382, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that patients with a more positive attitude toward participating in decision-making are more prepared to make treatment decisions. This aligns with the findings of Ma[16], who reported that cancer patients with high DMR are more actively involved in care decisions. The key factor is patients\u0026rsquo; ability to understand medical information and recognize their role in managing their health.\u003c/p\u003e\n \u003cp\u003eAccording to the CSM, patients\u0026rsquo; knowledge about chemotherapy directly influences their willingness to participate, which shapes their decision-making behavior[11]. When patients receive clear, accurate information - such as treatment plans and strategies for managing side effects - their motivation to engage increases. This often leads to active behaviors like asking questions and sharing treatment preferences.\u003c/p\u003e\n \u003cp\u003eTo support this, healthcare providers should establish regular assessments of DMR and adjust education or support strategies as needed. This can improve shared decision-making between providers, patients, and families, and help ensure better treatment engagement during chemotherapy.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e4.2.3 Lower levels of decision conflict are associated with higher DMR\u003c/h2\u003e\n \u003cp\u003eThis study found a negative correlation between decisional conflict and DMR among lung cancer patients during chemotherapy (r = -0.164, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that patients with less conflict tend to be more prepared to make treatment decisions. This supports the findings of Sui [23], who observed that patients with high DMR often communicate better with healthcare providers and can align medical information with their personal needs, leading to improved treatment outcomes.\u003c/p\u003e\n \u003cp\u003eAlthough the cross-sectional design limits causal interpretation, the CSM suggests a possible two-way relationship: high decisional conflict may reduce readiness by increasing uncertainty, while higher readiness may lower conflict by improving information processing. Future longitudinal research is needed to explore this relationship further.\u003c/p\u003e\n \u003cp\u003eClinically, this association highlights the importance of reducing decisional conflict to improve readiness. Nurses can support patients by providing clear information, strengthening communication, and helping clarify expectations. For those with high conflict, targeted strategies - such as addressing cost concerns or fears about side effects - can be developed. Involving family members in these interventions may also help improve DMR, treatment adherence, and overall quality of life.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e4.3 Study Limitation\u003c/h2\u003e\n \u003cp\u003ethis study is limited by its single-center, cross-sectional design. Future research should involve multi-center, interdisciplinary studies to develop strategies that reduce decisional conflict and enhance participation attitudes, thereby further improving decision readiness and patient outcomes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e4.4 Clinical Implications\u003c/h2\u003e\n \u003cp\u003eThis study highlights the moderate level of DMR among lung cancer patients during chemotherapy and identifies economic status, place of residence, decisional conflict, and decision participation attitude as significant influencing factors. These findings suggest several implications for psycho-oncology practice:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTargeted Decision Support Interventions\u003c/strong\u003e: Clinicians should integrate structured decision support interventions tailored to patients\u0026rsquo; socioeconomic background. Patients with lower income or limited access to resources may benefit from simplified educational materials and individualized counseling to improve health literacy and engagement.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStrengthening Communication in Shared Decision-Making (SDM)\u003c/strong\u003e: Given the strong association between decision participation attitude and readiness, oncology nurses and psychosocial professionals should foster a participatory environment that encourages patients to express their preferences and concerns, especially during key chemotherapy decision points.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAddressing Decisional Conflict through Psychoeducation\u003c/strong\u003e: The negative correlation between decisional conflict and DMR suggests the need for early screening and psychological support for patients exhibiting indecision or ambivalence. Tools such as DCS can be used in clinical settings to identify high-risk patients and trigger early psycho-oncological interventions.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCultural and Contextual Sensitivity\u003c/strong\u003e: The finding that rural patients demonstrated higher DMR than urban counterparts underscores the importance of understanding sociocultural dynamics in decision-making. Clinical teams should engage family members and respect local values, especially in collectivist settings, to enhance patient participation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRoutine DMR Monitoring and Adaptive Care Plans\u003c/strong\u003e: DMR is a dynamic process that may fluctuate throughout the chemotherapy course. Incorporating regular DMR assessments into clinical workflows allows timely adjustment of supportive care strategies, ensuring that patients remain engaged and confident in their treatment journey.\u003c/p\u003e\n \u003cp\u003eBy embedding these strategies into clinical pathways, oncology teams can empower patients, reduce psychological distress related to decision-making, and ultimately promote better treatment adherence and quality of life for individuals facing complex cancer care decisions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study found that lung cancer patients during chemotherapy have a moderate level of DMR, influenced by factors such as economic status, place of residence, decisional conflict, and DPA. During treatment, patients face not only physical and emotional burdens but also uncertainties about treatment outcomes and pressure from family and social contexts. To improve decision readiness and treatment adherence, healthcare providers should implement targeted interventions - including health education, psychological support, and enhanced clinical monitoring - to ultimately improve patients\u0026rsquo; quality of life.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that human research participants provided informed consent for publication of the data in all tables.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization: Yan Wang, Jinping Li, Xin Sun. Data curation: Yan Wang, Xin Sun, Jinping Li, Yumeng Quan. Formal analysis: Jinping Li, Yumeng Quan. Investigation: Jinping Li, Minfeng Zhai, Xuenan Hu, Panrong Wang. Methodology: Xin Sun, Yan Wang, Jinping Li, Yumeng Quan, Panrong Wang. Visualization: Xin Sun, Yan Wang. Writing - original draft: Yan Wang. Writing - review \u0026amp; editing: Yan Wang, Jinping Li, Yumeng Quan. Funding acquisition: Yan Wang. Project administration: Yan Wang \u0026amp; Xin Sun. Supervision: Xin Sun.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, \u0026amp; Jemal A (2022) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 229\u0026ndash;263\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan B, Zheng R, Zeng H, et al (2024) Cancer incidence and mortality in China, 2022. 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Jinan: Shandong University\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"influencing factors, decision - making readiness, lung cancer, CSM model, chemotherapy","lastPublishedDoi":"10.21203/rs.3.rs-7403827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7403827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aimed to investigate the current status of decision - making readiness (DMR) among lung cancer patients during chemotherapy and to explore its influencing factors, with the goal of informing targeted clinical interventions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional survey was conducted among 461 lung cancer patients during chemotherapy at a tertiary oncology hospital in China. Data were collected using the Decision Readiness Scale (C-PrepDM), Decisional Conflict Scale (DCS), and the Patient Participation in Treatment Decision-Making Attitude Questionnaire, along with demographic and clinical information. Hierarchical multiple regression analysis was performed to identify significant predictors of DMR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe overall level of DMR was moderate among the participants. Hierarchical regression revealed that economic status, place of residence, decisional conflict, and decision participation attitude were significant predictors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Patients with higher income and those residing in rural or township areas showed better DMR. A positive attitude toward participation in decision-making was positively correlated with DMR (r\u0026thinsp;=\u0026thinsp;0.382, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while decisional conflict was negatively correlated (r=\u0026ndash;0.164, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eDMR in lung cancer patients during chemotherapy is influenced by both socioeconomic and psychosocial factors. Enhancing patient education, promoting active decision participation, and reducing decisional conflict through tailored communication strategies may strengthen patients\u0026rsquo; readiness to engage in their treatment planning. Future multi-center and longitudinal studies are needed to further validate these findings.\u003c/p\u003e","manuscriptTitle":"Current status and influencing factors of decision-making readiness among lung cancer patients during chemotherapy: a cross-sectional study based on CSM model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 17:54:29","doi":"10.21203/rs.3.rs-7403827/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0617fc8c-1758-4cf7-8f1e-37d1b47f6c3e","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T03:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 17:54:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7403827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7403827","identity":"rs-7403827","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}