The Impact of Health Beliefs on Treatment Decision Conflict through Treatment Expectations Mediation in Lumbar Disc Herniation Patients

The Impact of Health Beliefs on Treatment Decision Conflict through Treatment Expectations Mediation in Lumbar Disc Herniation Patients | 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 Article The Impact of Health Beliefs on Treatment Decision Conflict through Treatment Expectations Mediation in Lumbar Disc Herniation Patients Yuan Tian, Shao-Hua Chen, Rui-Peng Song, Gao-Ding Jia, Shi-Na Cheng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6733962/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Background: Low back pain (LBP) is a leading cause of disability worldwide, with lumbar disc herniation (LDH) being a prevalent contributor. Treatment strategies for LDH range from conservative management to surgical decompression for persistent or neurocompressive cases, making effective treatment decision-making critical. This study investigates the impact of the Health Belief Scale (HBS) on decisional conflict scale (DCS) regarding treatment choices in patients with LDH. Methods: A cross-sectional survey was administered to 707 consecutively recruited patients with LDH. Three key constructs were assessed using validated instruments: health beliefs (Health Belief Model scale), DCS, and treatment expectations (Illness Perception Questionnaire-Revised). First, the goodness-of-fit of the hypothesized theoretical framework was evaluated through structural equation modeling (SEM). Subsequently, descriptive statistics and Pearson correlation analyses were conducted to examine intervariable relationships. Finally, the mediation effect of treatment expectations was tested using bias-corrected bootstrap procedures. Results: Health beliefs demonstrated a significant negative association with DCS (r = -0.66, p < 0.001), indicating that stronger health beliefs correlated with reduced decision-making difficulties. Similarly, treatment expectations exhibited a moderate inverse relationship with DCS (r = -0.32, p < 0.001). Mediation analysis identified that treatment expectations were associated with a partial mediating pattern in the relationship between health beliefs and DCS (B = -0.013, SE = 0.005, 95% CI: -0.024 to -0.006), reflecting an observed correlational pathway that accounts for 19.7% of the total association. Conclusion: This study provides novel insights into the psychosocial determinants of treatment decision-making in patients with LDH, highlighting the critical role of health beliefs and treatment expectations. These findings have significant clinical implications, suggesting that enhancing patients’ health literacy and calibrating realistic treatment expectations may be associated with more informed decision-making, with potential to improve therapeutic outcomes; however, causal inferences cannot be drawn from cross-sectional data. Future interventions should integrate belief-cognitive components into shared decision-making frameworks to optimize patient-centered care. Biological sciences/Psychology Health sciences/Diseases Health sciences/Health care Health sciences/Medical research lumbar disc herniation low back pain health belief model decisional conflict scale treatment decision-making patient expectations Figures Figure 1 1 Introduction Lumbar disc herniation (LDH) refers to the rupture of the annulus fibrosus of the intervertebral disc, leading to protrusion of the nucleus pulposus, compression of spinal nerves and cauda equina, and consequent inflammatory reactions, resulting in clinical symptoms such as pain and neurological dysfunction [ 1 ]. Low back pain is the leading cause of disability worldwide, and LDH is one of its most common contributors [ 2 ], with a global prevalence of 7.62% [ 3 ]. In China, over 300 million individuals have lumbar spine disorders, and approximately 15.2% have been diagnosed with LDH. The peak incidence occurs between the ages of 30 and 50 years, and the prevalence is on the increase due to changing lifestyles and work patterns. The associated pain and functional impairment severely compromise patients’ quality of life [ 4 ]. Current first-line treatments for LDH include conservative management and surgical interventions [ 5 , 6 ]. Studies report that symptoms in most patients improve with 6–12 weeks of conservative management [ 7 , 8 ]. Conservative approaches encompass bed rest, pharmacotherapy, exercise therapy, epidural injections, lumbar traction, and traditional Chinese medicine [ 9 – 11 ]. However, patients with persistent functional impairment, severely reduced quality of life unresponsive to 3–6 months of conservative treatment, or progressive neurological compression require timely surgical decompression [ 12 , 13 ]. Treatment selection—whether conservative or surgical—is influenced by multiple factors. While conservative management is safer and more suitable for mild or acute cases, its prolonged duration may be a disadvantage to patients with severe pain or disability. On the other hand, surgical intervention, though effective, raises safety concerns for some individuals [ 14 ]. In this context, informed treatment decision-making is critical. However, decision conflict—a psychological state of uncertainty when weighing risks, benefits, and personal values—may hinder optimal choices [ 15 – 18 ]. The Health Belief Model (HBM), a key framework for understanding health behaviors, posits that an individual’s perceptions and beliefs about health threats shape their behavioral choices [ 19 ]. The Health Belief Model (HBM) posits that individuals’ health-related decisions are shaped by their perceptions of susceptibility, severity, benefits, and barriers [ 20 ]. In orthopedic contexts, HBM has been applied to explain patients’ adherence to rehabilitation programs [ 21 ] and surgical decision-making [ 22 ]. For instance, Herrmann et al. [ 23 ]demonstrated that female patients perceived benefits and barriers of risk-reducing surgery significantly predicted their ovarian removal decisions, highlighting the utility of HBM in complex medical choices. Similarly, health beliefs may influence patients with LDH as they decide between the choices of conservative versus surgical options through analogous cognitive pathways. These findings suggest that health beliefs may be negatively associated with treatment decision conflict (Hypothesis 1), based on observed correlational patterns in prior research. Treatment expectations—patients’ anticipated outcomes regarding cure likelihood, efficacy, and quality of life—also play a pivotal role in decision-making. Expectations significantly shape treatment preferences, adherence, and psychological states. In chronic disease populations, high expectations correlate with reduced symptoms and improved quality of life [ 24 ]. Conversely, unmet expectations may trigger disappointment, depression, or decision-making dilemmas [ 25 ]. Studies propose that treatment expectations may act as a potential bridge in the correlational relationship between health beliefs and decision conflict (Fig. 1 ). In prior research, patients often report selecting therapies that align with perceived health gains shaped by belief-driven expectations [ 26 ], suggesting that expectations may exhibit a mediating pattern in this correlational relationship (Hypothesis 2). Treatment expectations have been extensively studied across chronic conditions, with consistent evidence linking them to decision-making processes. For example, in musculoskeletal disorders, positive expectations toward physical therapy correlate with higher adherence and reduced decisional uncertainty [ 27 ], while in surgical contexts, unmet expectations about pain relief predict post-treatment regret [ 28 ]. Specifically in LDH, preliminary research indicates that patients with realistic expectations for conservative treatment (e.g., gradual symptom improvement) are less likely to report decision conflict when choosing between surgery and non-surgical options [ 29 ]. These studies highlight that treatment expectations operate within a complex network of psychosocial factors, underscoring the need to explore their role in the correlational pathway between health beliefs and decision conflict in LDH. While prior research has explored factors influencing treatment decision conflict, the interplay between health beliefs, treatment expectations, and decision conflict in LDH remains underexamined. This study aims to clarify the impact of health beliefs on treatment decision conflict in patients with LDH, with treatment expectations as a mediator. The findings may provide valuable insights and evidence to support informed decision-making for this population. 2 Methods 2.1 Study Participants This study recruited participants via convenience sampling for a cross-sectional questionnaire survey. The inclusion criteria were as follows: (1) diagnosis of lumbar disc herniation (LDH) confirmed by lumbar MRI or CT scans; (2) imaging evidence of disc protrusion, extrusion, or sequestration; (3) age ≥ 18 years; and (4) voluntary participation. The exclusion criteria included individuals with communication barriers The survey was anonymous, and all participants provided informed consent prior to completing the questionnaires. A total of 743 questionnaires were collected, with 707 valid responses (validity rate: 95.15%). 2.2 Data Collection 2.2.1 Demographic Characteristics Demographic data were collected using a structured questionnaire that covered eight parameters: (a) gender, (b) marital status, (c) age, (d) education level, (e) health insurance type, (f) employment status, (g) annual household income per capita, and (h) residential area. 2.2.2 Measurement of Treatment Decision Conflict The decision conflict scale (DCS), developed by O’Connor et al. (1995) [ 30 ] and cross-culturally adapted into Chinese by Li Yu [ 31 ], was used to assess treatment decision conflict. This 16-item scale comprises five subscales: Informed (3 items), Values Clarity (3 items), Support (3 items), Uncertainty (3 items), and Effective Decision-Making (4 items). Responses were rated on a 5-point Likert scale (0 = strongly agree to 4 = strongly disagree). The total scores are calculated by averaging item scores and multiplying them by 25 to yield a 0–100 scale, with higher scores indicating greater decision conflict. The Chinese version demonstrated excellent internal consistency (Cronbach’s α = 0.897). 2.2.3 Measurement of Health Beliefs Health beliefs were measured using the revised Chinese version of the Health Belief Scale (HBS). The original scale, translated by Wu Yuanjianyun [ 32 ], is a widely recognized tool for assessing health beliefs. Ji Shaoyan et al. [ 33 ]conducted cross-cultural adaptation and validation, reporting strong reliability (test-retest reliability = 0.889, split-half reliability = 0.936) and internal consistency (Cronbach’s α = 0.967). The 48-item scale includes five subscales: Personal Health Beliefs, Perceived Ability to Implement, Perceived Control, Perceived Resource Utilization, and Perceived Threats. Items are rated on a 5-point Likert scale (1 = very weak to 5 = very strong), with higher total scores reflecting stronger health beliefs. 2.2.4 Measurement of Treatment Expectations The Treatment Expectation Questionnaire (TEX-Q), developed by Shedden-Mora et al. (2019) [ 34 ]at the University Medical Center Hamburg-Eppendorf (UKE), Germany, and adapted into Chinese by Yang Lina [ 35 ], was employed. This 11-item tool assesses six dimensions: Treatment Benefit, Positive Impact, Adverse Events, Negative Impact, Treatment Process, and Behavioral Control. Responses are scored on an 11-point Likert scale (0–10), with items 7–11 being reverse-scored. Total scores are calculated as the mean of all items, ranging from 0 (extremely negative expectations) to 10 (extremely positive expectations). The Chinese version exhibited excellent reliability (Cronbach’s α = 0.903 overall; 0.842–0.924 for subscales; split-half reliability = 0.958). 2.3 Data Analysis Data analysis for this study was conducted using R software (version 4.3.3; R Core Team, 2024) on Windows 11 x64 (build 26100). Descriptive statistics were used to summarize the key characteristics of clinical nurses. Continuous variables were presented as mean ± standard deviation (M ± SD) and categorical variables as frequencies and percentages. Independent sample t-tests or one-way ANOVA were used to examine the impact of different demographic characteristics on Innovative Behavior. According to Hair et al. (2010) [ 36 ], the correlation coefficient between individual items and the total scale score needed to be at least 0.5 for scale validity and appropriateness. Mediation analysis was conducted using the mediation package (version 4.5.0; Tingley D et al., 2014) to examine the mediating effects [ 37 ]. 2.4 Ethics This study was approved by the hospital ethics committee (approval number: 2025SZSYLCYJ0404) and complied with the ethical standards of the declaration of Helsinki. written informed consent was obtained from each of the patients prior to participation. 3 Results 3.1 Participant characteristics The sample comprised 707 consecutively recruited patients with LDH, of whom 63.08% were male and 36.92% were female. Regarding marital status, 68.74% were married while 31.26% were unmarried. Furthermore, regarding the educational background of the participants, 22.63% held a bachelor’s degree or higher, 11.59% had an associate degree, 36.21% completed high school or vocational secondary education, 19.09% had a junior high school education, and 10.47% had an elementary school education or below. In terms of employment status, 53.89% were employed, 30.12% were retired, and 15.98% were unemployed. Regarding insurance coverage, 54.17% had BMI insurance, 28.85% had NCMS insurance, and 16.97% had URBMI insurance. Residential distribution showed that 59.69% lived in urban areas, while 40.31% resided in non-urban areas. The time since the first onset of symptoms was distributed as follows: 28.71% within 3 months, 21.36% between 3 to 6 months, 28.85% between 6 to 12 months, and 21.07% over 1 year. Regarding the first medical consultation, 37.91% visited outpatient departments of tertiary hospitals, 34.80% chose to visit the outpatient departments of secondary or community hospitals, 13.86% were hospitalized in secondary hospitals, while 13.44% were hospitalized in tertiary hospitals. Among all participants, 84.58% had previously sought medical care for lumbar disc herniation-related issues, while 15.42% had not. Finally, the annual income distribution revealed that 21.78% earned over ¥150,000, 20.23% earned between ¥80,000 and ¥150,000, 29.99% earned between ¥30,000 and ¥80,000, and 28.01% earned less than ¥30,000 annually (Table 1 ). 3.2 The level of treatment decision conflict, health beliefs, and treatment expectations, and univariate analysis of treatment decision conflict The scores of DCS, HBS, and TEX of the recruited patients with LDH were 25.748 ± 14.241, 168.356 ± 33.543, and 101.960 ± 14.242, respectively (Table 2 ). The univariate analysis for DCS revealed that all general categorical variables included in this study showed significant between-group differences (p < 0.05), except for whether the participants had previously sought medical attention for lumbar disc herniation (i.e., consulted a doctor). Specifically, these significant variables included gender, marital status, education level, insurance type, employment status, annual income, residential area, time since symptom onset, and the hospital type for the first medical consultation. All variables demonstrating significant between-group differences were included as covariates in subsequent analyses (Table 3 ). 3.3 Correlations of treatment decision conflict, health beliefs, and treatment expectations Correlation analysis revealed significant associations among the DCS, HBS, and TEX. Health beliefs (M = 168.36, SD = 33.54) showed a negative correlation with DCS (r = -0.66, p < 0.001) and a positive correlation with treatment expectations (r = 0.22, p < 0.001). Treatment expectations (M = 101.96, SD = 14.24) were negatively correlated with DCS (r = -0.32, p < 0.001). Additionally, age, SXH, OTH, and HPH demonstrated significant correlations with either treatment expectations or DCS. Expectations were negatively correlated with DCS but positively correlated with treatment expectations. Notably, variables significantly associated with DCS.T and TEX.T were also included as covariates in subsequent analyses to control for potential confounding effects (Table 4 ). In this study, common method bias (CMB) was not a significant concern, as the first common factor explained only 26.50% of the variance, which is below the critical threshold of 40% set by Harman’s single-factor test, indicating no substantial CMB. 3.4 The mediating role of treatment expectations on the relationships between health beliefs and treatment decision conflict Regression analyses sequentially examined the correlational relationships among HBS, TEX, and DCS. The results demonstrated the following: 1) Health beliefs were significantly and negatively associated with DCS (β = -0.55, p < 0.001), accounting for 50.1% of the variance in DCS; 2) 2) Health beliefs were positively associated with treatment expectations (β = 0.18, p < 0.001), explaining 22.5% of the variance in TEX; 3) Both health beliefs and treatment expectations negatively predicted DCS - with health beliefs showing a stronger effect (β = -0.59, p < 0.001) compared to treatment expectations (β = -0.16, p < 0.001). Overall, they explained 52.1% of the variance in DCS. All models controlled for covariates in the analyses (Table 5 ). The mediation analysis examined the indirect effect of the HBS on DCS through TEX. The results revealed a statistically significant indirect effect of HBS on DCS through TEX (B = -0.013, S.E. = 0.005, 95% CI [-0.024, -0.006]). Simultaneously, the model demonstrated that the direct effect of HBS on DCS remained significant after controlling for TEX’s influence on DCS (B = -0.223, S.E.= 0.022, 95% CI [-0.266, -0.179]). These findings indicate that treatment expectations exhibit a partial mediating pattern in the correlational relationship between health beliefs and DCS, based on cross-sectional data. Although mediation was statistically significant, the small effect suggests other unmeasured factors may contribute to DCS (Table 6 ). 4 Discussion This study analyzed 707 participants to explore the relationship between health beliefs (HBS), treatment expectations (TEX), and DCS to assess the interactions among these variables and their impact on patients’ decision-making processes. This study’s findings demonstrated that health beliefs are significantly negatively associated with DCS in patients with lumbar disc herniation, consistent with prior correlational research [ 21 , 22 ]. Health beliefs were found to be a significant negative predictor of DCS (β = -0.55, p < 0.001), accounting for 50.1% of the variance in DCS. These findings align with those of previous research [ 21 ], indicating that stronger health beliefs are associated with lower feelings of conflict when facing medical decisions. Health beliefs correlate with reduced perceived uncertainty, which may be attributed to patients reporting greater confidence in treatment options, in turn correlating with lower DCS [ 22 ]; however, temporal ordering cannot be established. This result underscores that strengthening patients’ health beliefs could be an effective strategy to reduce DCS in clinical practice. Additionally, this study found that patients’ perceptions of disease severity, treatment benefits, and treatment barriers significantly influenced their treatment choices. For example, patients who perceived their disease as severe and treatment benefits as significant were more likely to opt for aggressive treatment, whereas those who perceived more treatment barriers tended to choose conservative treatment or delay treatment. A study by Herrmann et al. [ 35 ] also showed that disease conditions can influence patients’ treatment decisions. Treatment expectations were significantly and negatively associated with DCS (β = -0.16, p < 0.001), aligning with prior studies showing that higher expectations correlate with reduced decisional conflict in chronic disease management [ 24 , 25 ]. Higher treatment expectations were associated with lower feelings of conflict during medical decision-making [ 24 ]. This result further supports the importance of treatment expectations in the decision-making process, suggesting that clinicians should fully consider patients’ expectations when formulating treatment plans to reduce DCS, consistent with the findings of Oswald LB [ 25 ]. Treatment expectations influence treatment outcomes by affecting patients’ subjective feelings and behavioral responses. For instance, positive expectations can enhance treatment effects, producing a placebo effect, while negative expectations may lead to a nocebo effect that may hinder treatment efficacy [ 26 ]. Expectations not only influence short-term treatment outcomes but may also have long-term effects on patients’ recovery and quality of life. Research indicates that positive expectations can promote patient recovery and improve treatment satisfaction and adherence [ 27 ]. Therefore, treatment expectations play a significant role in medical decision-making dilemmas by influencing patients’ psychological states, treatment behaviors, and clinicians decision-making processes. Understanding this mechanism can help optimize treatment plans, improve patient satisfaction and treatment outcomes, and reduce ethical dilemmas and complexities in medical decision-making. HBS exhibited a significant indirect associational pattern with DCS through TEX (B = -0.013, S.E. = 0.005, 95% CI [-0.024, -0.006]). Additionally, the model showed that after controlling for the effect of TEX on DCS, the direct effect of HBS on DCS remained significant (B = -0.223, S.E. = 0.022, 95% CI [-0.266, -0.179]). These results indicate that treatment expectations show a partial mediating pattern in the correlational relationship between HBS and DCS. It is important to note that this pattern reflects observed associations in cross-sectional data and does not imply causation. Health beliefs not only directly influence treatment decisions but also indirectly affect decisions by shaping patients’ treatment expectations. Additionally, patients’ expectations regarding treatment efficacy, process, and behavioral control serve as a bridge between health beliefs and treatment decisions. Health beliefs were significantly and positively associated with treatment expectations (β = 0.18, p < 0.001), accounting for 22.5% of the variance in treatment expectations in this cross-sectional sample. This suggests that stronger health beliefs are associated with higher expectations for treatment outcomes. Health beliefs may enhance positive attitudes toward treatment, thereby raising expectations for treatment efficacy. This implies that clinicians should pay attention to patients’ health beliefs when formulating treatment plans to improve their expectations, potentially enhancing treatment adherence and outcomes. This study highlights the importance of considering patients’ health beliefs and treatment expectations in clinical practice. Healthcare professionals should actively assess patients’ health beliefs, understand their perceptions of disease and treatment, and use effective communication and education to help patients develop positive treatment expectations, thereby facilitating decisions that promote recovery. Strengthening health beliefs fosters more rational treatment decisions, while positive treatment expectations can influence patients’ health beliefs and decisions. A comprehensive understanding of the interactions among health beliefs, treatment decisions, and expectations is crucial for developing personalized treatment plans and interventions in clinical practice. 4.1 Study Limitations A key limitation is the use of cross-sectional data to examine mediation, which precludes establishing temporal ordering of variables or definitive causal relationships. Mediation analyses in cross-sectional designs identify correlational patterns rather than causal pathways, meaning the observed mediating role of treatment expectations should be interpreted as an associational trend rather than a directional effect. Future longitudinal studies are needed to verify the temporal sequence of health beliefs, treatment expectations, and decision conflict, which would strengthen inferences about potential causal relationships. The sample was primarily drawn from a specific region, limiting generalizability to other populations, and future research could expand the sample scope to improve generalizability. Additionally, the influence of health beliefs and treatment expectations may vary across cultural contexts, and Future studies should endeavor to stratify patients into cultural geographic subgroups and incorporate longitudinal designs to address temporal limitations. Longitudinal studies could further investigate the causal relationships among health beliefs, treatment expectations, and DCS, providing new perspectives for medical decision-making support. Future studies should endeavor to stratify patients into cultural geographic subgroups. 4.2 Conclusion By analyzing data from 707 participants, this study revealed the relationships between health beliefs, treatment expectations, and DCS. The results showed that health beliefs were significantly and negatively associated with DCS and positively associated with treatment expectations in this cross-sectional sample.Treatment expectations partially mediated the relationship between health beliefs and DCS. These findings highlight that enhancing patients' health beliefs and calibrating realistic treatment expectations may be associated with reduced DCS in clinical practice, based on observed correlations; however, causal claims are not supported by cross-sectional data. Future research could explore other potential mediating variables and the stability of these relationships across different cultural contexts. Clinicians should strengthen patients’ health beliefs through education and communication to reduce conflicts in medical decision-making. When formulating treatment plans, clinicians should consider patients’ expectations and use proactive communication and information provision to improve their expectations of treatment outcomes. Given patients’ diverse socioeconomic backgrounds and health conditions, individualized treatment plans should be developed to meet specific needs and reduce DCS. This study provides new insights into the relationships among health beliefs, treatment expectations, and DCS and offers valuable guidance for clinical practice. Declarations 5 Conflict of Interest No conflict of interest has been declared by the authors. 6 Author Contributions Yuan Tian : Conceptualization, Methodology, Data curation, Formal analysis, Writing- Original draft preparation, Writing- Reviewing and Editing, Project administration Shao-Hua Chen: Writing- Original draft preparation, Writing- Reviewing and Editing, Supervision, Methodology Gao-Ding Jia : Conceptualization, Data curation, Software, Formal analysis R ui-Peng Song: Validation, Resources, Funding acquisition Shi-Na Cheng: Investigation, Data curation Bei-Bei Chen: Conceptualization, Investigation, Data curation Jia-Ying Song: Investigation, Data curation Jing-Yi Yang : Investigation, Data curation 7 Funding This study received funds from the Key Research and Development Project of Henan Province (No. 241111313800) and National key clinical specialty construction Project (Yu Wei Medical Letter [2023] No.30). 8 Acknowledgments The authors delivered their appreciations to all the personnel involved in the study and Patients with lumbar intervertebral disc protrusion participated in this study. Special thanks were given to Health Commission of Henan Province, to provide grant support for the Program. 9 Ethical approval Ethical approval was granted by Ethics Committee of The Third People's Hospital of Henan Province (2025SZSYLCYJ0404)and complied with the ethical standards of the declaration of Helsinki. Written informed consent was obtained from all participants. References Kreiner, D. S., Hwang, S. W. & Easa, J. E. North American Spine Society. An evidence-based clinical guideline for the diagnosis and treatment of lumbar disc herniation with radiculopathy. Spine J. 14 , 180–191. 10.1016/j.spinee.2013.08.003 (2013). Vialle, L. R., Vialle, E. N., Suárez Henao, J. E. & Giraldo, G. LUMBAR DISC HERNIATION. Revista Brasileira de Ortop. (English Edition) . 45 , 17–22. 10.1016/s2255-4971(15)30211-1 (2010). Knezevic, N. N., Candido, K. D. & Vlaeyen, J. W. S. Low back pain. Lancet 398 , 78–92 (2021). Peng, J., Yunning, L. & Jiangmei, L. Li Jie, Zeng Xinying. Burden of Disease in the Chinese Population From . (2005). Chinese Orthopaedic Association of Orthopaedic Rehabilitation Group. Clinical practice guideline for diagnosis and treatment of lumbar disc herniation. Chin. J. Orthop. 40 , 477–487. 10.3760/cma.j.cn121113-20200402-00213 (2020). Støttrup, C., Andresen, A. K., Ernst, C. & Andersen, M. Ø. Surgical treatment of lumbar disc herniation. Ugeskr Laeger . (2018). Deyo, R. A. & Mirza, S. K. Herniated lumbar intervertebral disk. N Engl. J. Med. 374 , 1763–1772. 10.1056/nejmcp1512658 (2016). Deyo, R. A., Diehl, A. K. & Rosenthal, M. How many days of bed rest for acute low back pain? N Engl. J. Med. 315 , 1064–1070. 10.1056/nejm198610233151705 (1986). Weiner, B. Treatment of lumbar disc herniation: Evidence-based practice. Int. J. Gen. Med. 3 , 209–214. 10.2147/ijgm.s12270 (2010). Buy, X. & Gangi, A. Percutaneous Treatment of Intervertebral Disc Herniation. Semin Intervent Rad . 27 , 148–159. 10.1055/s-0030-1253513 (2010). Sun, K. et al. A systematic review and meta-analysis for Chinese herbal medicine Duhuo Jisheng decoction in treatment of lumbar disc herniation. Medicine 99 , e19310. 10.1097/md.0000000000019310 (2020). Haro, H. et al. Japanese Orthopaedic Association (JOA) clinical practice guidelines on the management of lumbar disc herniation, third edition - secondary publication. J. Orthop. Sci. 27 , 31–78. 10.1016/j.jos.2021.07.028 (2021). Haiqing, M., Feifei, Z. & Siyi, C. Expert consensus on the implementation process of percutaneous lumbar endoscopic surgery for accelerated rehabilitation surgery. Chin. J. Bone Joint Surg. 12 , 641–651 (2019). Watters, W. C. & McGirt, M. J. An evidence-based review of the literature on the consequences of conservative versus aggressive discectomy for the treatment of primary disc herniation with radiculopathy. Spine J. 9 , 240–257. 10.1016/j.spinee.2008.08.005 (2008). Wosny, M. et al. Factors guiding Clinical Decision-Making in Genitourinary Oncology. Cancer Med. 13 , 70304. 10.1002/cam4.70304 (2024). Andkhoie, M., Meyer, D. & Szafron, M. Factors underlying treatment decision-making for localized prostate cancer in the U.S. and Canada: A scoping review using principal component analysis. CUAJ 13 10.5489/cuaj.5538 (2019). Basile, F. W. et al. Uncertainty in tuberculosis clinical decision-making: An umbrella review with systematic methods and thematic analysis. PLOS Glob Public. Health . 4 , e0003429. 10.1371/journal.pgph.0003429 (2024). Zambri, F. et al. Health Belief Model efficacy in explaining and predicting intention or uptake influenza vaccination during pregnancy. Ann. Ist Super Sanita . 58 , 285–292. 10.4415/ANN_22_04_09 (2022). Pecanac, K. E., Brown, R. L. & Kremsreiter, H. B. Decisional conflict during major medical treatment decision-making: a survey study. J. Gen. Intern. Med. 36 , 55–61. 10.1007/s11606-020-06125-1 (2020). Rosenstock, I. M. Historical origins of the health belief model. Health Educ. Quart. 2 , 328–335. 10.1177/109019817400200403 (1974). Nyland, J. et al. Health beliefs and rehabilitation adherence in orthopedic patients: a prospective study. J. Orthop. Nurs. 45 , 112–120 (2023). Herrmann, A., Hall, A. & Proietto, A. Using the Health Belief Model to explore surgical decision-making in high-risk women. BMC Womens Health . 18 , 184 (2018). Kreiner, D. S., Hwang, S. W. & Easa, J. E. Evidence-based guidelines for lumbar disc herniation management. Spine J. 14 , 180–191 (2014). Oswald, L. B. et al. Patient expectations of benefit from systemic treatments for metastatic prostate cancer. Cancer Med. 9 , 980–987. 10.1002/cam4.2783 (2019). Yang, M. et al. Nocebo expectations rather than placebo expectations affect topical pain relief: A randomized clinical trial. Biomed. Pharmacother . 175 , 116728. 10.1016/j.biopha.2024.116728 (2024). Buergler, S., Sezer, D., Gaab, J. & Locher, C. The roles of expectation, comparator, administration route, and population in open-label placebo effects: a network meta-analysis. Sci. Rep. 13 , 11827. 10.1038/s41598-023-39123-4 (2023). Hair, J. F., Anderson, R. E., Tatham, R. L. & Black, W. C. (2010). Tingley, D., Yamamoto, T., Hirose, K., Keele, L. & Imai, K. mediation: R Package for causal mediation analysis. J. Stat. Soft . 59 , 1–38. 10.18637/jss.v059.i05 (2014). Herrmann, A., Hall, A. & Proietto, A. Using the Health Belief Model to explore why women decide for or against the removal of their ovaries to reduce their risk of developing cancer. BMC Women's Health . 18 , 184. 10.1186/s12905-018-0673-2 (2018). O' AM. ConnorMedical Decision Making? 15:25–30. Li, Y. Research on the construction and application of a treatment decision-making aid plan for patients with early primary liver cancer (Second Military Medical University, 2017). Lihong, W. et al. Mediating effect of health beliefs between health knowledge and health behavior in patients with hypertension and stroke. Chin. J. Behav. Med. Brain Sci. 26 , 847–851. 10.3760/cma.j.issn.1674-6554.2017.09.016 (2017). Shaoyan, J. & Hui, Y. Cross-cultural debugging and reliability and validity evaluation of health belief scale. China Med. Innov. 139–141. 10.3969/j.issn.1674-4985.2013.09.075 (2013). Alberts, J. et al. Development of the generic, multidimensional Treatment Expectation Questionnaire (TEX-Q) through systematic literature review, expert surveys and qualitative interviews. BMJ Open. 10 , e036169. 10.1136/bmjopen-2019-036169 (2020). Yang, L. I. Na. The sinicization of the Treatment Expectation Questionnaire (TEX-Q) and its application in colorectal cancer patients.Nanchang University (2023). French, D. P. & Sim, J. Treatment expectations and adherence to physical therapy in chronic low back pain: A systematic review. J. Orthop. Sports Phys. Ther. 50 (5), 241–251 (2020). Smith, R. A. & Johnson, T. L. The impact of preoperative expectations on post-surgical decision regret in lumbar spine surgery. Spine 44 (12), 867–873 (2019). Zhang, L. & Wang, Y. Treatment expectations and decisional conflict in lumbar disc herniation: A pilot study. J. Clin. Nurs. 31 (3–4), 562–570 (2022). Tables Table 1. Descriptive statistics Variable n (%) Variable n (%) Sex Female 261 (36.917) Annual Income 8~15W 143 (20.226) Male 446 (63.083) >15W 154 (21.782) Marital No 221 (31.259) 12m 149 (21.075) college 160 (22.631) 6~12m 204 (28.854) Bachelor 256 (36.209) 3~6m 151 (21.358) Insurance URBMI 120 (16.973) <3m 203 (28.713) NCMS 204 (28.854) Having seen a doctor No 109 (15.417) BMI 383 (54.173) Yes 598 (84.583) Employment Unemployed 113 (15.983) The form of the first visit and the level of hospital 3Hosp 95 (13.437) Retired 213 (30.127) 2Hosp 98 (13.861) Working 381 (53.890) 2Outp 246 (34.795) 3Outp 268 (37.907) Note . SD and MAD are used to represent standard deviation and Median Absolute Deviation, respectively. Q1 and Q3 represent 25% and 75% percentile, respectively. DCS: Decision Conflict Scale, HBS: Health Belief Scale. TEX: Treatment Expectation Questionnaire 3Hosp: Hospitalization in tertiary hospital; 3Outp: Outpatient at tertiary hospital; 2Hosp: Hospitalization in secondary hospital; 2Outp: Outpatient at secondary hospital. Table 2. Descriptive statistics Variable n (%) Mean ± SD Median (Q1, Q3) DCS 707 25.748 ± 14.241 30 (16, 35) HBS 707 168.356 ± 33.543 154 (145, 189) TEX 707 101.960 ± 14.242 103 (98, 107) Table 3. Univariate analysis of decisional conflict by demographic characteristics (ANOVA/t-tests) Level n (%) M ± SD F/t Effect p Gender 3.262 0.265 0.001 Male 446 (63.08%) 27.130 ± 13.261 Female 261 (36.92%) 23.387 ± 15.519 Marital –6.351 –0.465 *** Yes 486 (68.74%) 23.724 ± 14.970 No 221 (31.26%) 30.199 ± 11.305 Education 11.059 0.150 *** Primary 74 (10.47%) 18.905 ± 15.121 Junior 82 (11.60%) 22.341 ± 13.588 Senior 135 (19.10%) 23.630 ± 14.974 College 160 (22.63%) 27.019 ± 15.629 Bachelor 256 (36.21%) 29.141 ± 11.618 Insurance 21.220 0.130 *** BMI 383 (54.17%) 28.478 ± 12.987 NCMS 204 (28.85%) 24.804 ± 14.268 URBMI 120 (16.97%) 18.642 ± 15.427 Employment 20.432 0.131 *** Unemployed 113 (15.98%) 16.929 ± 16.709 Working 381 (53.89%) 27.837 ± 13.114 Retired 213 (30.13%) 26.690 ± 13.015 Annual Income 24.122 0.159 *** 15W 154 (21.78%) 31.857 ± 10.017 Residence 3.156 0.243 0.002 City 422 (59.69%) 27.135 ± 14.008 non-City 285 (40.31%) 23.695 ± 14.358 Duration of disease 19.414 0.141 **** 1 years 149 (21.08%) 17.940 ± 17.923 Have seen a doctor –1.790 –0.187 0.075 Yes 598 (84.58%) 25.338 ± 14.205 No 109（15.42%） 28.000 ± 14.291 The form of the first visit and the level of hospital 3.785 0.039 0.011 3Outp 268 (37.91%) 24.377 ± 15.334 3Hosp 95 (13.44%) 24.874 ± 14.602 2Outp 246 (34.80%) 26.232 ± 13.802 2Hosp 98 (13.86%) 29.133 ± 11.086 Note . M and SD are used to represent mean and standard deviation, respectively. Effect sizes are Cohen's d, partial η² for independent sample t-test, one-way ANOVA, respectively. *, **, *** indicate p < 0.05, p < 0.01 and p < 0.001, respectively. Effect sizes: Cohen's d for t-tests; partial η² for ANOVA. Table 4. Means, standard deviations, and correlations with confidence intervals M SD DCS HBS TEX Age SXH OTH HPH TRH DCS 25.748 14.241 1 HBS 168.356 33.543 –0.66*** 1 TEX 101.960 14.242 –0.322*** 0.202*** 1 Age 2.069 0.858 0.020 –0.165*** 0.197*** 1 SXH 3.429 1.953 –0.284*** 0.275*** 0.108 0.037 1 OTH 2.786 1.425 –0.024 –0.003 0.152** 0.06 0.094 1 HPH 7.253 3.566 0.170*** –0.233*** 0.088 0.116* 0.077 0.041 1 TRH 1.967 0.992 0.102 –0.191*** 0.054 0.103 0.062 0.065 0.252*** 1 Aspiration 4.352 1.697 –0.352*** 0.327*** 0.206*** –0.031 0.154*** –0.013 –0.152** –0.113* Note . M and SD are used to represent mean and standard deviation, respectively. DCS, DCS; HBS：Health Belief; TEX: Treatment Exceptition. *, **, *** indicate p < 0.05, p < 0.01 and p < 0.001, respectively. SXH: symptom; OTH: operative treatment; HPH: Hospital; TRH: Treat Table 5. Regression coefficients DV IV b S.E. T p β [Boot 95% CI] R 2 DCS HBS –0.236 0.015 –15.588 *** –0.555 [-0.625, -0.485] 0.501 TEX HBS 0.077 0.019 4.091 *** 0.182 [0.094, 0.269] 0.225 DCS HBS –0.223 0.015 –14.878 *** –0.526 [-0.595, -0.456] 0.521 TEX –0.163 0.030 –5.398 *** –0.163 [-0.222, -0.104] Note . A significant b-weight indicates the β-weight is also significant. b represents unstandardized coefficients, beta indicates the standardized coefficients. DV, dependent variable; IV, independent variable. CI or Square brackets are used to enclose the lower (LL) and upper (UL) limits of a Bias-corrected 95% confidence interval, respectively. *, **, *** indicate p < 0.05, p < 0.01 and p TEX->DCS(ab) –0.013 (0.005) –2.313 0.000 [-0.024, -0.006] HBS->DCS(c') –0.223 (0.022) –10.014 0.000 [-0.266, -0.179] HBS->DCS(c) –0.236 (0.021) –11.088 0.000 [-0.275, -0.191] Note . B, mediation effect; S.E., standard error; CI or Square brackets, Bias-corrected 95% confidence interval. S.E. and CI are estimated based on 1000 Bootstrap samples Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Aug, 2025 Reviews received at journal 29 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviews received at journal 06 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers invited by journal 06 Aug, 2025 Submission checks completed at journal 04 Aug, 2025 First submitted to journal 22 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6733962","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":498268971,"identity":"95de4e82-691c-408f-b31d-45a3660cb392","order_by":0,"name":"Yuan Tian","email":"","orcid":"","institution":"The Third People's Hospital of Henan Province","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Tian","suffix":""},{"id":498268972,"identity":"fff132d6-d539-4e6a-b1c6-06c6b344f23b","order_by":1,"name":"Shao-Hua Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDACCSBmbGBgMADSBz8Y2MixsbcfIFoL42OJgjRjPp4zCURrYTbg+XA4cZ6EgwFeHfyzm489/LnDTs5cIvmZhIQBc3qbBEMCw4+KbbgtuXMs3Zj3TLKx5Yw0M4kCA7bcNunGA4w9Z27j1GIgkWMmzdjGnLjhRoIZ0Bae3DaZAwnMjG34tOR/k/zZVg/Ukv5NgsdAIp1NIsGAgJYcNgnetsNALTnGBjwGBgkEtUjcSDOT5m07bmxw5k3hYwmDBMM2YCAfxOcX/hnJz4AOq5YzOJ6+4eCHP//l5dvbDz74UYFbC3ZwgET1o2AUjIJRMArQAABjD1cS1SSxZwAAAABJRU5ErkJggg==","orcid":"","institution":"Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shao-Hua","middleName":"","lastName":"Chen","suffix":""},{"id":498268973,"identity":"357f2f3f-1b06-42dd-a5e1-74ada72ae4a2","order_by":2,"name":"Rui-Peng Song","email":"","orcid":"","institution":"The Third People's Hospital of Henan Province","correspondingAuthor":false,"prefix":"","firstName":"Rui-Peng","middleName":"","lastName":"Song","suffix":""},{"id":498268974,"identity":"912da29a-fe3b-4684-8467-55c163697f03","order_by":3,"name":"Gao-Ding Jia","email":"","orcid":"","institution":"Shijiazhuang University","correspondingAuthor":false,"prefix":"","firstName":"Gao-Ding","middleName":"","lastName":"Jia","suffix":""},{"id":498268975,"identity":"7ad7ec82-c249-494a-b035-fc78e465b649","order_by":4,"name":"Shi-Na Cheng","email":"","orcid":"","institution":"The Third People’s Hospital of Henan Province","correspondingAuthor":false,"prefix":"","firstName":"Shi-Na","middleName":"","lastName":"Cheng","suffix":""},{"id":498268976,"identity":"7b7ad757-83c8-4a28-a511-302411cf435b","order_by":5,"name":"Bei-Bei Chen","email":"","orcid":"","institution":"The Third People’s Hospital of Henan Province","correspondingAuthor":false,"prefix":"","firstName":"Bei-Bei","middleName":"","lastName":"Chen","suffix":""},{"id":498268977,"identity":"29b3957c-9e0c-4736-9092-0acc833fcde7","order_by":6,"name":"Jia-Ying Song","email":"","orcid":"","institution":"The Third People’s Hospital of Henan Province","correspondingAuthor":false,"prefix":"","firstName":"Jia-Ying","middleName":"","lastName":"Song","suffix":""},{"id":498268978,"identity":"2934acfa-0452-41e8-854d-c48b31b8d6e1","order_by":7,"name":"Jing-Yi Yang","email":"","orcid":"","institution":"The Third People's Hospital of Henan Province","correspondingAuthor":false,"prefix":"","firstName":"Jing-Yi","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-05-23 14:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6733962/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6733962/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-27025-6","type":"published","date":"2025-11-28T15:58:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88794188,"identity":"9e0702bc-fac6-49e1-a027-b41fd5a8584a","added_by":"auto","created_at":"2025-08-11 13:15:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13595,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTheoretical frameworkTables\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6733962/v1/b7ca0c36ad010f49408f4f29.png"},{"id":97179438,"identity":"de8938d6-8297-4bdd-8cc2-085aab3cc0ec","added_by":"auto","created_at":"2025-12-01 16:15:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1177280,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6733962/v1/eb07f11b-b3d4-4f89-b010-03084ee7dfd7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Health Beliefs on Treatment Decision Conflict through Treatment Expectations Mediation in Lumbar Disc Herniation Patients","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLumbar disc herniation (LDH) refers to the rupture of the annulus fibrosus of the intervertebral disc, leading to protrusion of the nucleus pulposus, compression of spinal nerves and cauda equina, and consequent inflammatory reactions, resulting in clinical symptoms such as pain and neurological dysfunction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Low back pain is the leading cause of disability worldwide, and LDH is one of its most common contributors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], with a global prevalence of 7.62% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In China, over 300\u0026nbsp;million individuals have lumbar spine disorders, and approximately 15.2% have been diagnosed with LDH. The peak incidence occurs between the ages of 30 and 50 years, and the prevalence is on the increase due to changing lifestyles and work patterns. The associated pain and functional impairment severely compromise patients\u0026rsquo; quality of life [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCurrent first-line treatments for LDH include conservative management and surgical interventions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies report that symptoms in most patients improve with 6\u0026ndash;12 weeks of conservative management [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Conservative approaches encompass bed rest, pharmacotherapy, exercise therapy, epidural injections, lumbar traction, and traditional Chinese medicine [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, patients with persistent functional impairment, severely reduced quality of life unresponsive to 3\u0026ndash;6 months of conservative treatment, or progressive neurological compression require timely surgical decompression [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Treatment selection\u0026mdash;whether conservative or surgical\u0026mdash;is influenced by multiple factors. While conservative management is safer and more suitable for mild or acute cases, its prolonged duration may be a disadvantage to patients with severe pain or disability. On the other hand, surgical intervention, though effective, raises safety concerns for some individuals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this context, informed treatment decision-making is critical. However, decision conflict\u0026mdash;a psychological state of uncertainty when weighing risks, benefits, and personal values\u0026mdash;may hinder optimal choices [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Health Belief Model (HBM), a key framework for understanding health behaviors, posits that an individual\u0026rsquo;s perceptions and beliefs about health threats shape their behavioral choices [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The Health Belief Model (HBM) posits that individuals\u0026rsquo; health-related decisions are shaped by their perceptions of susceptibility, severity, benefits, and barriers [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In orthopedic contexts, HBM has been applied to explain patients\u0026rsquo; adherence to rehabilitation programs [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and surgical decision-making [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For instance, Herrmann et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]demonstrated that female patients perceived benefits and barriers of risk-reducing surgery significantly predicted their ovarian removal decisions, highlighting the utility of HBM in complex medical choices. Similarly, health beliefs may influence patients with LDH as they decide between the choices of conservative versus surgical options through analogous cognitive pathways. These findings suggest that health beliefs may be negatively associated with treatment decision conflict (Hypothesis 1), based on observed correlational patterns in prior research.\u003c/p\u003e\u003cp\u003eTreatment expectations\u0026mdash;patients\u0026rsquo; anticipated outcomes regarding cure likelihood, efficacy, and quality of life\u0026mdash;also play a pivotal role in decision-making. Expectations significantly shape treatment preferences, adherence, and psychological states. In chronic disease populations, high expectations correlate with reduced symptoms and improved quality of life [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Conversely, unmet expectations may trigger disappointment, depression, or decision-making dilemmas [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Studies propose that treatment expectations may act as a potential bridge in the correlational relationship between health beliefs and decision conflict (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In prior research, patients often report selecting therapies that align with perceived health gains shaped by belief-driven expectations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], suggesting that expectations may exhibit a mediating pattern in this correlational relationship (Hypothesis 2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTreatment expectations have been extensively studied across chronic conditions, with consistent evidence linking them to decision-making processes. For example, in musculoskeletal disorders, positive expectations toward physical therapy correlate with higher adherence and reduced decisional uncertainty [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], while in surgical contexts, unmet expectations about pain relief predict post-treatment regret [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Specifically in LDH, preliminary research indicates that patients with realistic expectations for conservative treatment (e.g., gradual symptom improvement) are less likely to report decision conflict when choosing between surgery and non-surgical options [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These studies highlight that treatment expectations operate within a complex network of psychosocial factors, underscoring the need to explore their role in the correlational pathway between health beliefs and decision conflict in LDH.\u003c/p\u003e\u003cp\u003eWhile prior research has explored factors influencing treatment decision conflict, the interplay between health beliefs, treatment expectations, and decision conflict in LDH remains underexamined. This study aims to clarify the impact of health beliefs on treatment decision conflict in patients with LDH, with treatment expectations as a mediator. The findings may provide valuable insights and evidence to support informed decision-making for this population.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Participants\u003c/h2\u003e\u003cp\u003eThis study recruited participants via convenience sampling for a cross-sectional questionnaire survey. The inclusion criteria were as follows: (1) diagnosis of lumbar disc herniation (LDH) confirmed by lumbar MRI or CT scans; (2) imaging evidence of disc protrusion, extrusion, or sequestration; (3) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; and (4) voluntary participation. The exclusion criteria included individuals with communication barriers The survey was anonymous, and all participants provided informed consent prior to completing the questionnaires. A total of 743 questionnaires were collected, with 707 valid responses (validity rate: 95.15%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Demographic Characteristics\u003c/h2\u003e\u003cp\u003eDemographic data were collected using a structured questionnaire that covered eight parameters: (a) gender, (b) marital status, (c) age, (d) education level, (e) health insurance type, (f) employment status, (g) annual household income per capita, and (h) residential area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Measurement of Treatment Decision Conflict\u003c/h2\u003e\u003cp\u003eThe decision conflict scale (DCS), developed by O\u0026rsquo;Connor et al. (1995) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and cross-culturally adapted into Chinese by Li Yu [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], was used to assess treatment decision conflict. This 16-item scale comprises five subscales: Informed (3 items), Values Clarity (3 items), Support (3 items), Uncertainty (3 items), and Effective Decision-Making (4 items). Responses were rated on a 5-point Likert scale (0\u0026thinsp;=\u0026thinsp;strongly agree to 4\u0026thinsp;=\u0026thinsp;strongly disagree). The total scores are calculated by averaging item scores and multiplying them by 25 to yield a 0\u0026ndash;100 scale, with higher scores indicating greater decision conflict. The Chinese version demonstrated excellent internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.897).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Measurement of Health Beliefs\u003c/h2\u003e\u003cp\u003eHealth beliefs were measured using the revised Chinese version of the Health Belief Scale (HBS). The original scale, translated by Wu Yuanjianyun [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], is a widely recognized tool for assessing health beliefs. Ji Shaoyan et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]conducted cross-cultural adaptation and validation, reporting strong reliability (test-retest reliability\u0026thinsp;=\u0026thinsp;0.889, split-half reliability\u0026thinsp;=\u0026thinsp;0.936) and internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.967). The 48-item scale includes five subscales: Personal Health Beliefs, Perceived Ability to Implement, Perceived Control, Perceived Resource Utilization, and Perceived Threats. Items are rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;very weak to 5\u0026thinsp;=\u0026thinsp;very strong), with higher total scores reflecting stronger health beliefs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4 Measurement of Treatment Expectations\u003c/h2\u003e\u003cp\u003eThe Treatment Expectation Questionnaire (TEX-Q), developed by Shedden-Mora et al. (2019) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]at the University Medical Center Hamburg-Eppendorf (UKE), Germany, and adapted into Chinese by Yang Lina [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], was employed. This 11-item tool assesses six dimensions: Treatment Benefit, Positive Impact, Adverse Events, Negative Impact, Treatment Process, and Behavioral Control. Responses are scored on an 11-point Likert scale (0\u0026ndash;10), with items 7\u0026ndash;11 being reverse-scored. Total scores are calculated as the mean of all items, ranging from 0 (extremely negative expectations) to 10 (extremely positive expectations). The Chinese version exhibited excellent reliability (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.903 overall; 0.842\u0026ndash;0.924 for subscales; split-half reliability\u0026thinsp;=\u0026thinsp;0.958).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Analysis\u003c/h2\u003e\u003cp\u003eData analysis for this study was conducted using R software (version 4.3.3; R Core Team, 2024) on Windows 11 x64 (build 26100). Descriptive statistics were used to summarize the key characteristics of clinical nurses. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) and categorical variables as frequencies and percentages. Independent sample t-tests or one-way ANOVA were used to examine the impact of different demographic characteristics on Innovative Behavior. According to Hair et al. (2010) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], the correlation coefficient between individual items and the total scale score needed to be at least 0.5 for scale validity and appropriateness. Mediation analysis was conducted using the mediation package (version 4.5.0; Tingley D et al., 2014) to examine the mediating effects [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Ethics\u003c/h2\u003e\u003cp\u003e This study was approved by the hospital ethics committee (approval number: 2025SZSYLCYJ0404) and complied with the ethical standards of the declaration of Helsinki. written informed consent was obtained from each of the patients prior to participation.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Participant characteristics\u003c/h2\u003e\n\u003cp\u003eThe sample comprised 707 consecutively recruited patients with LDH, of whom 63.08% were male and 36.92% were female. Regarding marital status, 68.74% were married while 31.26% were unmarried. Furthermore, regarding the educational background of the participants, 22.63% held a bachelor\u0026rsquo;s degree or higher, 11.59% had an associate degree, 36.21% completed high school or vocational secondary education, 19.09% had a junior high school education, and 10.47% had an elementary school education or below.\u003c/p\u003e\n\u003cp\u003eIn terms of employment status, 53.89% were employed, 30.12% were retired, and 15.98% were unemployed. Regarding insurance coverage, 54.17% had BMI insurance, 28.85% had NCMS insurance, and 16.97% had URBMI insurance. Residential distribution showed that 59.69% lived in urban areas, while 40.31% resided in non-urban areas.\u003c/p\u003e\n\u003cp\u003eThe time since the first onset of symptoms was distributed as follows: 28.71% within 3 months, 21.36% between 3 to 6 months, 28.85% between 6 to 12 months, and 21.07% over 1 year. Regarding the first medical consultation, 37.91% visited outpatient departments of tertiary hospitals, 34.80% chose to visit the outpatient departments of secondary or community hospitals, 13.86% were hospitalized in secondary hospitals, while 13.44% were hospitalized in tertiary hospitals.\u003c/p\u003e\n\u003cp\u003eAmong all participants, 84.58% had previously sought medical care for lumbar disc herniation-related issues, while 15.42% had not. Finally, the annual income distribution revealed that 21.78% earned over \u0026yen;150,000, 20.23% earned between \u0026yen;80,000 and \u0026yen;150,000, 29.99% earned between \u0026yen;30,000 and \u0026yen;80,000, and 28.01% earned less than \u0026yen;30,000 annually (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cstrong\u003e3.2 The level of treatment decision conflict, health beliefs, and treatment expectations, and univariate analysis of treatment decision conflict\u003c/strong\u003e\u003cbr /\u003e\n\u003cp\u003eThe scores of DCS, HBS, and TEX of the recruited patients with LDH were 25.748\u0026thinsp;\u0026plusmn;\u0026thinsp;14.241, 168.356\u0026thinsp;\u0026plusmn;\u0026thinsp;33.543, and 101.960\u0026thinsp;\u0026plusmn;\u0026thinsp;14.242, respectively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The univariate analysis for DCS revealed that all general categorical variables included in this study showed significant between-group differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except for whether the participants had previously sought medical attention for lumbar disc herniation (i.e., consulted a doctor). Specifically, these significant variables included gender, marital status, education level, insurance type, employment status, annual income, residential area, time since symptom onset, and the hospital type for the first medical consultation. All variables demonstrating significant between-group differences were included as covariates in subsequent analyses (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Correlations of treatment decision conflict, health beliefs, and treatment expectations\u003c/strong\u003e Correlation analysis revealed significant associations among the DCS, HBS, and TEX. Health beliefs (M\u0026thinsp;=\u0026thinsp;168.36, SD\u0026thinsp;=\u0026thinsp;33.54) showed a negative correlation with DCS (r = -0.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a positive correlation with treatment expectations (r\u0026thinsp;=\u0026thinsp;0.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Treatment expectations (M\u0026thinsp;=\u0026thinsp;101.96, SD\u0026thinsp;=\u0026thinsp;14.24) were negatively correlated with DCS (r = -0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eAdditionally, age, SXH, OTH, and HPH demonstrated significant correlations with either treatment expectations or DCS. Expectations were negatively correlated with DCS but positively correlated with treatment expectations. Notably, variables significantly associated with DCS.T and TEX.T were also included as covariates in subsequent analyses to control for potential confounding effects (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn this study, common method bias (CMB) was not a significant concern, as the first common factor explained only 26.50% of the variance, which is below the critical threshold of 40% set by Harman\u0026rsquo;s single-factor test, indicating no substantial CMB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 The mediating role of treatment expectations on the relationships between health beliefs and treatment decision conflict\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegression analyses sequentially examined the correlational relationships among HBS, TEX, and DCS. The results demonstrated the following: 1) Health beliefs were significantly and negatively associated with DCS (\u0026beta; = -0.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), accounting for 50.1% of the variance in DCS; 2) 2) Health beliefs were positively associated with treatment expectations (\u0026beta;\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining 22.5% of the variance in TEX; 3) Both health beliefs and treatment expectations negatively predicted DCS - with health beliefs showing a stronger effect (\u0026beta; = -0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to treatment expectations (\u0026beta; = -0.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Overall, they explained 52.1% of the variance in DCS. All models controlled for covariates in the analyses (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe mediation analysis examined the indirect effect of the HBS on DCS through TEX. The results revealed a statistically significant indirect effect of HBS on DCS through TEX (B = -0.013, S.E. = 0.005, 95% CI [-0.024, -0.006]). Simultaneously, the model demonstrated that the direct effect of HBS on DCS remained significant after controlling for TEX\u0026rsquo;s influence on DCS (B = -0.223, S.E.= 0.022, 95% CI [-0.266, -0.179]). These findings indicate that treatment expectations exhibit a partial mediating pattern in the correlational relationship between health beliefs and DCS, based on cross-sectional data. Although mediation was statistically significant, the small effect suggests other unmeasured factors may contribute to DCS (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study analyzed 707 participants to explore the relationship between health beliefs (HBS), treatment expectations (TEX), and DCS to assess the interactions among these variables and their impact on patients\u0026rsquo; decision-making processes. This study\u0026rsquo;s findings demonstrated that health beliefs are significantly negatively associated with DCS in patients with lumbar disc herniation, consistent with prior correlational research [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Health beliefs were found to be a significant negative predictor of DCS (β = -0.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), accounting for 50.1% of the variance in DCS. These findings align with those of previous research [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], indicating that stronger health beliefs are associated with lower feelings of conflict when facing medical decisions. Health beliefs correlate with reduced perceived uncertainty, which may be attributed to patients reporting greater confidence in treatment options, in turn correlating with lower DCS [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]; however, temporal ordering cannot be established. This result underscores that strengthening patients\u0026rsquo; health beliefs could be an effective strategy to reduce DCS in clinical practice. Additionally, this study found that patients\u0026rsquo; perceptions of disease severity, treatment benefits, and treatment barriers significantly influenced their treatment choices. For example, patients who perceived their disease as severe and treatment benefits as significant were more likely to opt for aggressive treatment, whereas those who perceived more treatment barriers tended to choose conservative treatment or delay treatment. A study by Herrmann et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] also showed that disease conditions can influence patients\u0026rsquo; treatment decisions.\u003c/p\u003e\u003cp\u003eTreatment expectations were significantly and negatively associated with DCS (β = -0.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), aligning with prior studies showing that higher expectations correlate with reduced decisional conflict in chronic disease management [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Higher treatment expectations were associated with lower feelings of conflict during medical decision-making [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This result further supports the importance of treatment expectations in the decision-making process, suggesting that clinicians should fully consider patients\u0026rsquo; expectations when formulating treatment plans to reduce DCS, consistent with the findings of Oswald LB [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Treatment expectations influence treatment outcomes by affecting patients\u0026rsquo; subjective feelings and behavioral responses. For instance, positive expectations can enhance treatment effects, producing a placebo effect, while negative expectations may lead to a nocebo effect that may hinder treatment efficacy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Expectations not only influence short-term treatment outcomes but may also have long-term effects on patients\u0026rsquo; recovery and quality of life. Research indicates that positive expectations can promote patient recovery and improve treatment satisfaction and adherence [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, treatment expectations play a significant role in medical decision-making dilemmas by influencing patients\u0026rsquo; psychological states, treatment behaviors, and clinicians decision-making processes. Understanding this mechanism can help optimize treatment plans, improve patient satisfaction and treatment outcomes, and reduce ethical dilemmas and complexities in medical decision-making.\u003c/p\u003e\u003cp\u003eHBS exhibited a significant indirect associational pattern with DCS through TEX (B = -0.013, S.E. = 0.005, 95% CI [-0.024, -0.006]). Additionally, the model showed that after controlling for the effect of TEX on DCS, the direct effect of HBS on DCS remained significant (B = -0.223, S.E. = 0.022, 95% CI [-0.266, -0.179]). These results indicate that treatment expectations show a partial mediating pattern in the correlational relationship between HBS and DCS. It is important to note that this pattern reflects observed associations in cross-sectional data and does not imply causation. Health beliefs not only directly influence treatment decisions but also indirectly affect decisions by shaping patients\u0026rsquo; treatment expectations. Additionally, patients\u0026rsquo; expectations regarding treatment efficacy, process, and behavioral control serve as a bridge between health beliefs and treatment decisions. Health beliefs were significantly and positively associated with treatment expectations (β\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), accounting for 22.5% of the variance in treatment expectations in this cross-sectional sample. This suggests that stronger health beliefs are associated with higher expectations for treatment outcomes. Health beliefs may enhance positive attitudes toward treatment, thereby raising expectations for treatment efficacy. This implies that clinicians should pay attention to patients\u0026rsquo; health beliefs when formulating treatment plans to improve their expectations, potentially enhancing treatment adherence and outcomes.\u003c/p\u003e\u003cp\u003eThis study highlights the importance of considering patients\u0026rsquo; health beliefs and treatment expectations in clinical practice. Healthcare professionals should actively assess patients\u0026rsquo; health beliefs, understand their perceptions of disease and treatment, and use effective communication and education to help patients develop positive treatment expectations, thereby facilitating decisions that promote recovery. Strengthening health beliefs fosters more rational treatment decisions, while positive treatment expectations can influence patients\u0026rsquo; health beliefs and decisions. A comprehensive understanding of the interactions among health beliefs, treatment decisions, and expectations is crucial for developing personalized treatment plans and interventions in clinical practice.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Study Limitations\u003c/h2\u003e\u003cp\u003eA key limitation is the use of cross-sectional data to examine mediation, which precludes establishing temporal ordering of variables or definitive causal relationships. Mediation analyses in cross-sectional designs identify correlational patterns rather than causal pathways, meaning the observed mediating role of treatment expectations should be interpreted as an associational trend rather than a directional effect. Future longitudinal studies are needed to verify the temporal sequence of health beliefs, treatment expectations, and decision conflict, which would strengthen inferences about potential causal relationships.\u003c/p\u003e\u003cp\u003eThe sample was primarily drawn from a specific region, limiting generalizability to other populations, and future research could expand the sample scope to improve generalizability. Additionally, the influence of health beliefs and treatment expectations may vary across cultural contexts, and Future studies should endeavor to stratify patients into cultural geographic subgroups and incorporate longitudinal designs to address temporal limitations. Longitudinal studies could further investigate the causal relationships among health beliefs, treatment expectations, and DCS, providing new perspectives for medical decision-making support. Future studies should endeavor to stratify patients into cultural geographic subgroups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Conclusion\u003c/h2\u003e\u003cp\u003eBy analyzing data from 707 participants, this study revealed the relationships between health beliefs, treatment expectations, and DCS. The results showed that health beliefs were significantly and negatively associated with DCS and positively associated with treatment expectations in this cross-sectional sample.Treatment expectations partially mediated the relationship between health beliefs and DCS. These findings highlight that enhancing patients' health beliefs and calibrating realistic treatment expectations may be associated with reduced DCS in clinical practice, based on observed correlations; however, causal claims are not supported by cross-sectional data. Future research could explore other potential mediating variables and the stability of these relationships across different cultural contexts.\u003c/p\u003e\u003cp\u003eClinicians should strengthen patients\u0026rsquo; health beliefs through education and communication to reduce conflicts in medical decision-making. When formulating treatment plans, clinicians should consider patients\u0026rsquo; expectations and use proactive communication and information provision to improve their expectations of treatment outcomes. Given patients\u0026rsquo; diverse socioeconomic backgrounds and health conditions, individualized treatment plans should be developed to meet specific needs and reduce DCS. This study provides new insights into the relationships among health beliefs, treatment expectations, and DCS and offers valuable guidance for clinical practice.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003e5 Conflict of Interest\u003c/h3\u003e\n\u003cp\u003eNo conflict of interest has been declared by the authors.\u003c/p\u003e\n\u003ch3\u003e6 Author Contributions\u003c/h3\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eYuan Tian\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Conceptualization, Methodology, Data curation, Formal analysis, Writing- Original draft preparation, Writing- Reviewing and Editing, Project administration\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eShao-Hua Chen:\u003c/strong\u003eWriting- Original draft preparation, Writing- Reviewing and Editing,\u0026nbsp;Supervision,\u0026nbsp;Methodology\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGao-Ding Jia\u0026nbsp;:\u003c/strong\u003e Conceptualization, Data curation, Software, Formal analysis\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003eui-Peng Song:\u003c/strong\u003e Validation, Resources, Funding acquisition\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eShi-Na Cheng:\u003c/strong\u003e Investigation, Data curation\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBei-Bei Chen:\u003c/strong\u003e Conceptualization, \u0026nbsp; Investigation, Data curation\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eJia-Ying Song:\u0026nbsp;\u003c/strong\u003eInvestigation, Data curation\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eJing-Yi Yang :\u003c/strong\u003eInvestigation, Data curation\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e7\u0026nbsp;Funding\u003c/h3\u003e\n\u003cp\u003eThis study received funds from the Key Research and Development Project of Henan Province (No. 241111313800) and National key clinical specialty construction Project (Yu Wei Medical Letter [2023] No.30).\u003c/p\u003e\n\u003ch3\u003e8\u0026nbsp;Acknowledgments\u003c/h3\u003e\n\u003cp\u003eThe authors delivered their appreciations to all the personnel involved in the study and Patients with lumbar intervertebral disc protrusion\u0026nbsp;participated in this study. Special thanks were given to\u0026nbsp;Health Commission of Henan Province, to provide grant support for the Program.\u003c/p\u003e\n\u003ch3\u003e9 Ethical approval\u003c/h3\u003e\n\u003cp skip=\"true\"\u003eEthical approval was granted by Ethics Committee of The Third People\u0026apos;s Hospital of Henan Province (2025SZSYLCYJ0404)and complied with the ethical standards of the declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKreiner, D. S., Hwang, S. W. \u0026amp; Easa, J. E. North American Spine Society. An evidence-based clinical guideline for the diagnosis and treatment of lumbar disc herniation with radiculopathy. \u003cem\u003eSpine J.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 180\u0026ndash;191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.spinee.2013.08.003\u003c/span\u003e\u003cspan address=\"10.1016/j.spinee.2013.08.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVialle, L. R., Vialle, E. N., Su\u0026aacute;rez Henao, J. E. \u0026amp; Giraldo, G. LUMBAR DISC HERNIATION. \u003cem\u003eRevista Brasileira de Ortop. (English Edition)\u003c/em\u003e. \u003cb\u003e45\u003c/b\u003e, 17\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s2255-4971(15)30211-1\u003c/span\u003e\u003cspan address=\"10.1016/s2255-4971(15)30211-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnezevic, N. N., Candido, K. D. \u0026amp; Vlaeyen, J. W. S. Low back pain. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e398\u003c/b\u003e, 78\u0026ndash;92 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng, J., Yunning, L. \u0026amp; Jiangmei, L. Li Jie, Zeng Xinying. \u003cem\u003eBurden of Disease in the Chinese Population From\u003c/em\u003e. (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChinese Orthopaedic Association of Orthopaedic Rehabilitation Group. Clinical practice guideline for diagnosis and treatment of lumbar disc herniation. \u003cem\u003eChin. J. Orthop.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 477\u0026ndash;487. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3760/cma.j.cn121113-20200402-00213\u003c/span\u003e\u003cspan address=\"10.3760/cma.j.cn121113-20200402-00213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSt\u0026oslash;ttrup, C., Andresen, A. K., Ernst, C. \u0026amp; Andersen, M. \u0026Oslash;. \u003cem\u003eSurgical treatment of lumbar disc herniation. Ugeskr Laeger\u003c/em\u003e. (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeyo, R. A. \u0026amp; Mirza, S. K. Herniated lumbar intervertebral disk. \u003cem\u003eN Engl. J. Med.\u003c/em\u003e \u003cb\u003e374\u003c/b\u003e, 1763\u0026ndash;1772. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/nejmcp1512658\u003c/span\u003e\u003cspan address=\"10.1056/nejmcp1512658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeyo, R. A., Diehl, A. K. \u0026amp; Rosenthal, M. How many days of bed rest for acute low back pain? \u003cem\u003eN Engl. J. Med.\u003c/em\u003e \u003cb\u003e315\u003c/b\u003e, 1064\u0026ndash;1070. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/nejm198610233151705\u003c/span\u003e\u003cspan address=\"10.1056/nejm198610233151705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1986).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeiner, B. Treatment of lumbar disc herniation: Evidence-based practice. \u003cem\u003eInt. J. Gen. Med.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 209\u0026ndash;214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/ijgm.s12270\u003c/span\u003e\u003cspan address=\"10.2147/ijgm.s12270\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuy, X. \u0026amp; Gangi, A. Percutaneous Treatment of Intervertebral Disc Herniation. \u003cem\u003eSemin Intervent Rad\u003c/em\u003e. \u003cb\u003e27\u003c/b\u003e, 148\u0026ndash;159. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1055/s-0030-1253513\u003c/span\u003e\u003cspan address=\"10.1055/s-0030-1253513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun, K. et al. A systematic review and meta-analysis for Chinese herbal medicine Duhuo Jisheng decoction in treatment of lumbar disc herniation. \u003cem\u003eMedicine\u003c/em\u003e \u003cb\u003e99\u003c/b\u003e, e19310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/md.0000000000019310\u003c/span\u003e\u003cspan address=\"10.1097/md.0000000000019310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaro, H. et al. Japanese Orthopaedic Association (JOA) clinical practice guidelines on the management of lumbar disc herniation, third edition - secondary publication. \u003cem\u003eJ. Orthop. Sci.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 31\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jos.2021.07.028\u003c/span\u003e\u003cspan address=\"10.1016/j.jos.2021.07.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaiqing, M., Feifei, Z. \u0026amp; Siyi, C. Expert consensus on the implementation process of percutaneous lumbar endoscopic surgery for accelerated rehabilitation surgery. \u003cem\u003eChin. J. Bone Joint Surg.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 641\u0026ndash;651 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatters, W. C. \u0026amp; McGirt, M. J. An evidence-based review of the literature on the consequences of conservative versus aggressive discectomy for the treatment of primary disc herniation with radiculopathy. \u003cem\u003eSpine J.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 240\u0026ndash;257. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.spinee.2008.08.005\u003c/span\u003e\u003cspan address=\"10.1016/j.spinee.2008.08.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWosny, M. et al. Factors guiding Clinical Decision-Making in Genitourinary Oncology. \u003cem\u003eCancer Med.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 70304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cam4.70304\u003c/span\u003e\u003cspan address=\"10.1002/cam4.70304\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndkhoie, M., Meyer, D. \u0026amp; Szafron, M. Factors underlying treatment decision-making for localized prostate cancer in the U.S. and Canada: A scoping review using principal component analysis. \u003cem\u003eCUAJ\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5489/cuaj.5538\u003c/span\u003e\u003cspan address=\"10.5489/cuaj.5538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBasile, F. W. et al. Uncertainty in tuberculosis clinical decision-making: An umbrella review with systematic methods and thematic analysis. \u003cem\u003ePLOS Glob Public. Health\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e, e0003429. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pgph.0003429\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgph.0003429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZambri, F. et al. Health Belief Model efficacy in explaining and predicting intention or uptake influenza vaccination during pregnancy. \u003cem\u003eAnn. Ist Super Sanita\u003c/em\u003e. \u003cb\u003e58\u003c/b\u003e, 285\u0026ndash;292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4415/ANN_22_04_09\u003c/span\u003e\u003cspan address=\"10.4415/ANN_22_04_09\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePecanac, K. E., Brown, R. L. \u0026amp; Kremsreiter, H. B. Decisional conflict during major medical treatment decision-making: a survey study. \u003cem\u003eJ. Gen. Intern. Med.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 55\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11606-020-06125-1\u003c/span\u003e\u003cspan address=\"10.1007/s11606-020-06125-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenstock, I. M. Historical origins of the health belief model. \u003cem\u003eHealth Educ. Quart.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 328\u0026ndash;335. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/109019817400200403\u003c/span\u003e\u003cspan address=\"10.1177/109019817400200403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1974).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNyland, J. et al. Health beliefs and rehabilitation adherence in orthopedic patients: a prospective study. \u003cem\u003eJ. Orthop. Nurs.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, 112\u0026ndash;120 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerrmann, A., Hall, A. \u0026amp; Proietto, A. Using the Health Belief Model to explore surgical decision-making in high-risk women. \u003cem\u003eBMC Womens Health\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 184 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKreiner, D. S., Hwang, S. W. \u0026amp; Easa, J. E. Evidence-based guidelines for lumbar disc herniation management. \u003cem\u003eSpine J.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 180\u0026ndash;191 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOswald, L. B. et al. Patient expectations of benefit from systemic treatments for metastatic prostate cancer. \u003cem\u003eCancer Med.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 980\u0026ndash;987. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cam4.2783\u003c/span\u003e\u003cspan address=\"10.1002/cam4.2783\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, M. et al. Nocebo expectations rather than placebo expectations affect topical pain relief: A randomized clinical trial. \u003cem\u003eBiomed. Pharmacother\u003c/em\u003e. \u003cb\u003e175\u003c/b\u003e, 116728. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biopha.2024.116728\u003c/span\u003e\u003cspan address=\"10.1016/j.biopha.2024.116728\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuergler, S., Sezer, D., Gaab, J. \u0026amp; Locher, C. The roles of expectation, comparator, administration route, and population in open-label placebo effects: a network meta-analysis. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 11827. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-39123-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-39123-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHair, J. F., Anderson, R. E., Tatham, R. L. \u0026amp; Black, W. C. (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTingley, D., Yamamoto, T., Hirose, K., Keele, L. \u0026amp; Imai, K. mediation: R Package for causal mediation analysis. \u003cem\u003eJ. Stat. Soft\u003c/em\u003e. \u003cb\u003e59\u003c/b\u003e, 1\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18637/jss.v059.i05\u003c/span\u003e\u003cspan address=\"10.18637/jss.v059.i05\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerrmann, A., Hall, A. \u0026amp; Proietto, A. Using the Health Belief Model to explore why women decide for or against the removal of their ovaries to reduce their risk of developing cancer. \u003cem\u003eBMC Women's Health\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 184. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12905-018-0673-2\u003c/span\u003e\u003cspan address=\"10.1186/s12905-018-0673-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO' AM. ConnorMedical Decision Making? 15:25\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, Y. \u003cem\u003eResearch on the construction and application of a treatment decision-making aid plan for patients with early primary liver cancer\u003c/em\u003e (Second Military Medical University, 2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLihong, W. et al. Mediating effect of health beliefs between health knowledge and health behavior in patients with hypertension and stroke. \u003cem\u003eChin. J. Behav. Med. Brain Sci.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, 847\u0026ndash;851. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3760/cma.j.issn.1674-6554.2017.09.016\u003c/span\u003e\u003cspan address=\"10.3760/cma.j.issn.1674-6554.2017.09.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShaoyan, J. \u0026amp; Hui, Y. Cross-cultural debugging and reliability and validity evaluation of health belief scale. \u003cem\u003eChina Med. Innov.\u003c/em\u003e 139\u0026ndash;141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3969/j.issn.1674-4985.2013.09.075\u003c/span\u003e\u003cspan address=\"10.3969/j.issn.1674-4985.2013.09.075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlberts, J. et al. Development of the generic, multidimensional Treatment Expectation Questionnaire (TEX-Q) through systematic literature review, expert surveys and qualitative interviews. \u003cem\u003eBMJ Open.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, e036169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2019-036169\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2019-036169\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, L. I. Na. The sinicization of the Treatment Expectation Questionnaire (TEX-Q) and its application in colorectal cancer patients.Nanchang University (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrench, D. P. \u0026amp; Sim, J. Treatment expectations and adherence to physical therapy in chronic low back pain: A systematic review. \u003cem\u003eJ. Orthop. Sports Phys. Ther.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e (5), 241\u0026ndash;251 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith, R. A. \u0026amp; Johnson, T. L. The impact of preoperative expectations on post-surgical decision regret in lumbar spine surgery. \u003cem\u003eSpine\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e (12), 867\u0026ndash;873 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, L. \u0026amp; Wang, Y. Treatment expectations and decisional conflict in lumbar disc herniation: A pilot study. \u003cem\u003eJ. Clin. Nurs.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (3\u0026ndash;4), 562\u0026ndash;570 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Descriptive statistics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 217px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 219px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e261 (36.917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eAnnual Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e8~15W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e143 (20.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e446 (63.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026gt;15W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e154 (21.782)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMarital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e221 (31.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;3W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e198 (28.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e486 (68.741)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e3~8W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e212 (29.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e74 (10.467)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003enon-City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e285 (40.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eJunior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e82 (11.598)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e422 (59.689)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e135 (19.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eDuration of disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026gt;12m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e149 (21.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ecollege\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e160 (22.631)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e6~12m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e204 (28.854)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eBachelor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e256 (36.209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e3~6m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e151 (21.358)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eURBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e120 (16.973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;3m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e203 (28.713)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNCMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e204 (28.854)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eHaving seen a doctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e109 (15.417)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e383 (54.173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e598 (84.583)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e113 (15.983)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eThe form of the first visit and the level of hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e3Hosp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e95 (13.437)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e213 (30.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e2Hosp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e98 (13.861)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e381 (53.890)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e2Outp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e246 (34.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e3Outp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e268 (37.907)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. SD and MAD are used to represent standard deviation and Median Absolute Deviation, respectively. Q1 and Q3 represent 25% and 75% percentile, respectively. DCS: Decision Conflict Scale, HBS: Health Belief Scale. TEX: Treatment Expectation Questionnaire \u0026nbsp; 3Hosp: Hospitalization in tertiary hospital; 3Outp: Outpatient at tertiary hospital; 2Hosp: Hospitalization in secondary hospital; 2Outp: Outpatient at secondary hospital.\u003c/p\u003e\n\u003cp\u003eTable 2. Descriptive statistics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eDCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e25.748 \u0026plusmn; 14.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e30 (16, 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eHBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e168.356 \u0026plusmn; 33.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e154 (145, 189)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eTEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e101.960 \u0026plusmn; 14.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e103 (98, 107)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. Univariate analysis of decisional conflict \u0026nbsp;by demographic characteristics (ANOVA/t-tests)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"632\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eM \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eF/t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e446 (63.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e27.130 \u0026plusmn; 13.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e261 (36.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e23.387 \u0026plusmn; 15.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eMarital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026ndash;6.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026ndash;0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e486 (68.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e23.724 \u0026plusmn; 14.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e221 (31.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e30.199 \u0026plusmn; 11.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e11.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e74 (10.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e18.905 \u0026plusmn; 15.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eJunior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e82 (11.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e22.341 \u0026plusmn; 13.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e135 (19.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e23.630 \u0026plusmn; 14.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eCollege\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e160 (22.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e27.019 \u0026plusmn; 15.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eBachelor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e256 (36.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e29.141 \u0026plusmn; 11.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e21.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e383 (54.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e28.478 \u0026plusmn; 12.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eNCMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e204 (28.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e24.804 \u0026plusmn; 14.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eURBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e120 (16.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e18.642 \u0026plusmn; 15.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e20.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e113 (15.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e16.929 \u0026plusmn; 16.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e381 (53.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e27.837 \u0026plusmn; 13.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e213 (30.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e26.690 \u0026plusmn; 13.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eAnnual Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e24.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026lt;3W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e198 (28.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e20.944 \u0026plusmn; 15.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3~8W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e212 (29.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e24.420 \u0026plusmn; 15.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e8~15W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e143 (20.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e27.790 \u0026plusmn; 12.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026gt;15W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e154 (21.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e31.857 \u0026plusmn; 10.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e422 (59.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e27.135 \u0026plusmn; 14.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003enon-City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e285 (40.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e23.695 \u0026plusmn; 14.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eDuration of disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e19.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026lt;3 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e203 (28.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e26.369 \u0026plusmn; 14.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3~6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e151 (21.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e26.901 \u0026plusmn; 12.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e6~12 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e204 (28.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e29.980 \u0026plusmn; 9.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026gt;1 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e149 (21.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e17.940 \u0026plusmn; 17.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eHave seen a doctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026ndash;1.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026ndash;0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e598 (84.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e25.338 \u0026plusmn; 14.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e109（15.42%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e28.000 \u0026plusmn; 14.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eThe form of the first visit and the level of hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3Outp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e268 (37.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e24.377 \u0026plusmn; 15.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3Hosp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e95 (13.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e24.874 \u0026plusmn; 14.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2Outp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e246 (34.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e26.232 \u0026plusmn; 13.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2Hosp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e98 (13.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e29.133 \u0026plusmn; 11.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. M and SD are used to represent mean and standard deviation, respectively. Effect sizes are Cohen\u0026apos;s d, partial \u0026eta;\u0026sup2; for independent sample t-test, one-way ANOVA, respectively. *, **, *** indicate p \u0026lt; 0.05, p \u0026lt; 0.01 and p \u0026lt; 0.001, respectively. Effect sizes: Cohen\u0026apos;s d for t-tests; partial \u0026eta;\u0026sup2; for ANOVA.\u003c/p\u003e\n\u003cp\u003eTable 4. Means, standard deviations, and correlations with confidence intervals\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eDCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eHBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eTEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eSXH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eOTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eHPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eTRH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eDCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e25.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e14.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eHBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e168.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e33.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ndash;0.66***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eTEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e101.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e14.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ndash;0.322***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.202***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ndash;0.165***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.197***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSXH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ndash;0.284***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.275***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eOTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ndash;0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ndash;0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.152**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eHPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e7.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e3.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.170***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ndash;0.233***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.116*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eTRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ndash;0.191***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.252***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eAspiration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e4.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ndash;0.352***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.327***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.206***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026ndash;0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.154***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026ndash;0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026ndash;0.152**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.113*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. M and SD are used to represent mean and standard deviation, respectively. DCS, DCS; HBS：Health Belief; TEX: Treatment Exceptition. *, **, *** indicate p \u0026lt; 0.05, p \u0026lt; 0.01 and p \u0026lt; 0.001, respectively. SXH: symptom; OTH: operative treatment; HPH: Hospital; TRH: Treat\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 5. Regression coefficients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[Boot 95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;15.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[-0.625, -0.485]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.094, 0.269]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;14.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[-0.595, -0.456]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;5.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[-0.222, -0.104]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. A significant b-weight indicates the \u0026beta;-weight is also significant. b represents unstandardized coefficients, beta indicates the standardized coefficients. DV, dependent variable; IV, independent variable. CI or Square brackets are used to enclose the lower (LL) and upper (UL) limits of a Bias-corrected 95% confidence interval, respectively. *, **, *** indicate p \u0026lt; 0.05, p \u0026lt; 0.01 and p \u0026lt; 0.001, respectively. \u0026beta;: standardized regression coefficient; b: unstandardized coefficient.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6. Mediation effects\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[Boot 95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBS-\u0026gt;TEX-\u0026gt;DCS(ab)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;2.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[-0.024, -0.006]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBS-\u0026gt;DCS(c\u0026apos;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;10.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[-0.266, -0.179]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBS-\u0026gt;DCS(c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;11.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[-0.275, -0.191]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. B, mediation effect; S.E., standard error; CI or Square brackets, Bias-corrected 95% confidence interval. S.E. and CI are estimated based on 1000 Bootstrap samples\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"lumbar disc herniation, low back pain, health belief model, decisional conflict scale, treatment decision-making, patient expectations","lastPublishedDoi":"10.21203/rs.3.rs-6733962/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6733962/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Low back pain (LBP) is a leading cause of disability worldwide, with lumbar disc herniation (LDH) being a prevalent contributor. Treatment strategies for LDH range from conservative management to surgical decompression for persistent or neurocompressive cases, making effective treatment decision-making critical. This study investigates the impact of the Health Belief Scale (HBS) on decisional conflict scale (DCS) regarding treatment choices in patients with LDH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional survey was administered to 707 consecutively recruited patients with LDH. Three key constructs were assessed using validated instruments: health beliefs (Health Belief Model scale), DCS, and treatment expectations (Illness Perception Questionnaire-Revised). First, the goodness-of-fit of the hypothesized theoretical framework was evaluated through structural equation modeling (SEM). Subsequently, descriptive statistics and Pearson correlation analyses were conducted to examine intervariable relationships. Finally, the mediation effect of treatment expectations was tested using bias-corrected bootstrap procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Health beliefs demonstrated a significant negative association with DCS (r = -0.66, p \u0026lt; 0.001), indicating that stronger health beliefs correlated with reduced decision-making difficulties. Similarly, treatment expectations exhibited a moderate inverse relationship with DCS (r = -0.32, p \u0026lt; 0.001). Mediation analysis identified that treatment expectations were associated with a partial mediating pattern in the relationship between health beliefs and DCS (B = -0.013, SE = 0.005, 95% CI: -0.024 to -0.006), reflecting an observed correlational pathway that accounts for 19.7% of the total association.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study provides novel insights into the psychosocial determinants of treatment decision-making in patients with LDH, highlighting the critical role of health beliefs and treatment expectations. These findings have significant clinical implications, suggesting that enhancing patients’ health literacy and calibrating realistic treatment expectations \u0026nbsp;may be associated with more informed decision-making, with potential to improve therapeutic outcomes; however, causal inferences cannot be drawn from cross-sectional data. Future interventions should integrate belief-cognitive components into shared decision-making frameworks to optimize patient-centered care.\u003c/p\u003e","manuscriptTitle":"The Impact of Health Beliefs on Treatment Decision Conflict through Treatment Expectations Mediation in Lumbar Disc Herniation Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 13:15:10","doi":"10.21203/rs.3.rs-6733962/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-29T18:23:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T04:30:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203973257462725759123318243839705058715","date":"2025-08-22T04:58:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-06T12:35:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49060583718765688059549159959268220446","date":"2025-08-06T11:13:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-06T10:01:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-04T06:36:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-22T13:51:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cad70d61-d11a-495e-9b0d-05bb19482330","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52925447,"name":"Biological sciences/Psychology"},{"id":52925448,"name":"Health sciences/Diseases"},{"id":52925449,"name":"Health sciences/Health care"},{"id":52925450,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-12-01T16:11:36+00:00","versionOfRecord":{"articleIdentity":"rs-6733962","link":"https://doi.org/10.1038/s41598-025-27025-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-28 15:58:35","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-08-11 13:15:10","video":"","vorDoi":"10.1038/s41598-025-27025-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-27025-6","workflowStages":[]},"version":"v1","identity":"rs-6733962","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6733962","identity":"rs-6733962","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}