Risk Factors of Decisional Conflict in People Living with Chronic Pain: a pan-Canadian survey.

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Florian Naye, Yannick Tousignant-Laflamme, Maxime Sasseville, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5417847/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Making decisions about chronic pain care is often challenging due to uncertainties, leading to decisional conflict when individuals do not receive the support and information they need. Shared decision-making interventions can help meet these needs; however, their effectiveness is inconsistent in the context of chronic pain. This study aimed to identify the decisional needs influencing decisional conflict among adults with chronic pain in Canada, to guide the development of more comprehensive interventions. In this pan-Canadian online survey, we measured decisional conflict related to the most difficult decision using the Decisional Conflict Scale (≥ 37.5 indicating clinically significant conflict) and assessed decisional needs based on the Ottawa Decision Support Framework. Of the 1,649 participants, 1,373 reported a Decisional Conflict Scale score. The mean age was 52 (SD = 16.4), with half of respondents being men (49.5%) and pain duration ranging from 3 months to 59 years. One-third (33.7%) experienced clinically significant decisional conflict. Seventeen risk factors were identified, including difficulty understanding healthcare information (OR = 2.43) and lack of prior knowledge of available options (OR = 2.03), while role congruence in decision-making was associated with reduced conflict (OR = 0.57). Future SDM interventions could be enhanced by targeting multiple risk factors of decisional conflict. Health sciences/Health care/Health services Health sciences/Health care/Patient education Shared decision-making Chronic pain Decisional conflict Patient-centred care Survey Figures Figure 1 INTRODUCTION Decision-making in pain care is characterized by uncertainty. People living with chronic pain face difficult decisions with multiple management approaches, often without a best option [ 1 ] . The decision-making requires trade-offs, affected by the uncertainty of evidence and outcomes [ 2 – 4 ] . Uncertainty in healthcare can lead to decisional conflict [ 5 ] . Recently, our team conducted a survey across all Canadian provinces to describe decision-making and decision-making needs in pain care [ 6 ] . We found that a third of people living with chronic in Canada experienced decisional conflict following a difficult decision for their condition [ 7 ] . Decisional conflict is defined as “a state of uncertainty about the course of action to take” [ 8 ] . Decisional conflict arises when there is a gap between the support and information people need to make a decision and what they actually receive from healthcare providers [ 9 ] . Decisional conflict is mainly measured in research with the Decisional Conflict Scale [ 9 – 11 ] . Above a certain score threshold on Decisional Conflict Scale, people are experiencing clinically significant decisional conflict (CSDC) [ 9 ] which is associated with decisional delay, departure from chosen option, decision regret, nervousness, and intention to sue physicians in cases of harms from chose option [ 12 ] . Reducing CSDC benefits both people living with chronic pain and clinicians, leading to improved clinical outcomes [ 13 ] . Shared decision-making interventions can reduce decisional conflict [ 10 , 14 ] . Authors of an updated Cochrane review from 2024 highlighted that patient decision aids improve the decision-making process and reduce decisional conflict across various conditions [ 10 ] . Patient decision aids effectiveness is inconsistent in the context of chronic musculoskeletal pain [ 15 ] . People living with chronic pain exhibit highly heterogeneous profiles [ 16 – 18 ] . The lack of understanding of individual and decision-making characteristics - or risk factors - leading to CSDC results in shared decision-making interventions not meeting people’s decisional needs. Identifying and targeting risk factors of CSDC will improve the development of comprehensive shared decision-making interventions that optimize decision-making processes and outcomes. The aim of this study was to identify risk factors of CSDC among people living with chronic pain. RESULTS Survey administration and attrition We randomly invited 31,545 members within Leger Marketing’s panel. The invitation view rate was 18.9% (5,949/3,1545). The participation rate was 44.8% (2,666/5,949) and the completion rate was 61.9% (1,649/2,666) – 1,649 people living with chronic pain completed the survey in four weeks from August 31, 2022 to September 28, 2022. We had no a priori information on the percentage of people living with chronic pain who did not experience difficult decisions (i.e., attrition). After data collection, 276 respondents (16.7%) did not report a most difficult decision leading to no information on decisional conflict – 1,373 reported a Decisional Conflict Scale score. Characteristics of the respondents We describe the respondents’ characteristics in Supplementary Table S1 . Of the 1,373 respondents who experienced a most difficult decision, mean age was 52 (SD=16.4). Half of respondents were men (49.5%), most lived in urban areas (88.2%), and pain duration ranged from 3 months to 59 years. Two thirds (68.1%) perceived assuming a collaborative role during the decision-making process and 71% reported having difficulty understanding what healthcare providers said about their medical condition. One third (33.7%) of the sample experienced CSDC (Decisional Conflict Scale score ≥37.5). Multilevel regression analysis From the intercept-only model, we calculated the mean ICC (i.e, intraclass correlation coefficient) from the imputed datasets and obtained 0.002 indicating that a small proportion of the total variance was explained by the provinces. Risk factors of CSDC (i.e., Decisional Conflict Scale score ≥37.5)) We present the results of the Ottawa Decision Support Framework (ODSF) model (hereafter ODSF model) in Figure 1 . We report details on the results for the ODSF model variables and the results of non-ODSF model variables in Supplementary Table S2. Out of 56 independent variables, we found 17 risk factors of CSDC of which seven were associated with an increased risk of CSDC and 10 were associated with a reduced risk. Half (47.1%) of the significant risk factors were ODSF-related variables. CAD : Canadian Dollar Risk factors associated with an increased risk of CSDC From the ODSF model, we found two variables statistically associated with increasing risk of CSDC: 1- having difficulty understanding what healthcare providers said about their condition compared to never having difficulty (“sometimes”: OR=2.43, 95%CI [1.65;3.58], “often”: OR=2.23, 95%CI [1.34;3.69]), and 2- no prior knowledge (OR=2.03, 95%CI [1.27;3.25]) or prior knowledge of certain options (OR=1.47, 95%CI [1.00;2.14]) compared to prior knowledge of all options. From the non-ODSF model, five variables were statistically associated with increasing risk of CSDC: 1- no congruence between the chosen and preferred option (OR=3.00, 95%CI [1.91;4.72]) or not having a preferred option compared to congruence (OR=1.97, 95%CI [1.31;2.98]), 2- being unsatisfied by their current health state (OR=1.99, 95%CI [1.39;2.84]) compared to satisfied, 3- first learned language different to English (OR=1.90, 95%CI [1.10;3.26]), 4- presence of pelvic pain (OR=1.72, 95%CI [1.06;2.78]), and 5- presence of low limb pain (OR=1.51, 95%CI [1.06;2.13]). Risk factors associated with a reduced risk of CSDC From the ODSF model, six variables were associated with reducing risk of CSDC: 1- decision about consulting a mental health professional (OR=0.42, 95%CI [0.18;0.95]) compared to decision about taking medication, 2- perception of assuming a collaborative role (OR=0.46, 95%CI [0.30;0.69]) compared to active role, 3- congruence between assumed and preferred role (OR=0.57, 95%CI [0.43;0.77]) compared to non-congruence, 4- higher decision self-efficacy (OR=0.65, 95%CI [0.59;0.70]), 5- higher quality of life (OR=0.71, 95%CI [0.63;0.81]), and 6- higher age (OR=0.80, 95%CI [0.68;0.94]). From the non-ODSF model, four variables were associated with reducing risk of CSDC: 1- presence of alcohol-related disorders (OR=0.41, 95%CI [0.17;0.99]), 2- perception of not being stressed during the consultation (OR=0.42, 95%CI [0.29;0.59]), 3- considering the consequences of the available options on work ability during the decision-making process (OR=0.44, 95%CI [0.29;0.67]), and 4- presence of hypertension (OR=0.63, 95%CI [0.41;0.96]). Sensitivity analysis We found similar results when comparing the complete case analysis with the imputed analysis. The main difference emerging from the multilevel linear regression analysis concerned health literacy and the contribution of always having difficulty to understand what healthcare providers said about their condition (ß=-7.79). We found the same result (OR=0.10, 95%CI [0.03;0.36]) with the multilevel binary logistic regression analysis with a DCS cut-off of 25. Supplementary Table S2 reported the results of all sensitivity analysis. DISCUSSION This pan-Canadian survey identified 17 risk factors of CSDC associated with difficult decisions for pain care. Half of the significant risk factors were ODSF-related variables. The risk factors with the highest odd ratios concerned congruence between the chosen and preferred role, having difficulty to understand what the healthcare providers said about their condition, and no prior knowledge of the available options. Based on these discoveries, we make four observations to improve shared decision-making interventions in pain care. Future shared decision-making interventions will need to target the decision-making process. Eight out of 17 risk factors are mainly related to the decision-making process from both the ODSF and non-ODSF models. For example, our data indicates that not reaching congruence between the chosen and preferred option in a clinical consultation is associated with twice the risk of a person experiencing CSDC. The importance of the decision-making characteristics is consistent with a recent systematic review [ 19 ] . Becerra-Pérez et al. reviewed the risk factors of decision regret, a consequence of decisional conflict, in 59 studies across multiple clinical contexts (e.g., oncology, family practice) [ 19 ] . Consistent with our results, the authors found that numerous risk factors of decisional regret were related with decision-making process such as a person’s role in making decisions [ 19 ] . From our finding (i.e., half of the significant risk factors were ODSF-related variables) and a previous overview of systematic reviews [ 20 ] , we argue that researchers should use decisional needs assessment based on the ODSF prior to developing future shared decision-making interventions. Future interventions will need to target role matching. We found that congruence between the assumed and preferred role could reduce the risk of CSDC. We see this protective factor as an easy target for improvement, as our previous result found that only 50% of people living with chronic pain in Canada perceived assuming a role that is congruent with their preferred one [ 7 ] . Targeting role matching could also have positive impacts, as it was showed that congruence between the assumed and preferred role can improve quality of life (i.e., one risk factor of CSDC) [ 21 ] . Matching collaborative role could also help to address another risk factor of CSDC: the congruence between the chosen and preferred option. Because collaborative role assumed deliberation on the preferences of a person and a clinician, the chosen option had greater chance to be aligned with the preferred one [ 10 , 22 ] . Addressing role matching during a consultation can easily be done with one validated question such as our survey did with the Control Preferences Scale [ 23 ] . We argue that clinicians could meet decisional needs for people living with chronic pain and reduce decisional conflict by aligning their assumed and preferred roles early in care. Future interventions will adapt to the person’s health literacy. Not understanding what the clinician said in a consultation is associated with an increased risk of CSDC. Targeting health literacy is an expanding field within the shared decision-making literature [ 24 – 26 ] . Low health literacy may lead to potential cognitive biases impacting the shared decision-making process [ 26 , 27 ] . Because low health literacy is prevalent [ 28 ] , shared decision-making interventions should integrate people with low health literacy in their co-development [ 29 ] . Including people with low literacy is an ethical foundation for shared decision-making as evidence showed that average readability of online support resources is higher than recommended for patient literature [ 30 – 32 ] . In a recent example in rheumatoid arthritis, authors built a co-design team including individuals with expertise in health literacy and patients with a large range of health literacy [ 33 ] . The decision aid they produced was tested in a 3-arm pilot study on 166 participants and reported promising results on improving decisional conflict [ 34 ] . Our sensitivity analyses suggest that systematic difficulty to understand what healthcare providers said is associated with a reduced risk of CSDC. This finding warrants further investigation in future health literacy studies. Future interventions will need to be designed longitudinally. Most interventions such as patient decision aids are used at a single time point (i.e., during the consultation) and that may explain why shared decision-making interventions in pain care have not reached their full potential. We found that having no prior knowledge of available options is associated with an increased risk of CSDC. In our survey, half of respondent indicated wanting to acquire knowledge of these options with their healthcare providers. These results outline the importance of education on available options to prepare a consultation as pain care involve numerous options that cannot be tackled in one “15-min” consultation. The Juvenile idiopathic arthritis Option MAP [ 35 ] aims to solve this issue with a decision aid that proposes pre-consultation stages with patient-reported outcomes, value clarification, and education on available options [ 35 ] . The person can select option preferences and report barriers to implementing these options. The summary can be used during the consultation to support the information exchange and ease the decision-making process. As pain care is already a complex endeavour [ 36 ] , artificial intelligence tools could support decision aids that would aggregate demographic characteristics, patient-reported outcomes, health system utilization, and comorbidities to the benefit of increasing engagement and personalization of pain care [ 37 ] . Strengths and limitations A strength of our study is the limited risk of coverage and participation biases due to our stratified proportional random sampling improving the representativeness of the sample. A second strength is the use of strategies to reduce the incentive bias. A limitation of our study is that Canadians from Territories are not represented in our survey as well as the 6% of the Canadian population not having Internet access at home (Canadian census [ 38 ] ). Future studies will need to replicate our findings in Indigenous people and other hardly reached population who traditionally have poor access to our Canadian health systems. Another limitation is the retrospective nature of the questions requiring recall of a clinical consultation. We minimized this recall bias by adjusting the models with respondent’s perception of the recall accuracy. Concerning our sample size, not all 1,649 respondents reported a Decisional Conflict Scale score. The attrition did not affect the representativeness of the sample ( Supplementary Table S3 ). A post hoc calculation adjusted with the measured prevalence of CSDC revealed sufficient statistical power to build our initial regression model including 25 independent variables instead of 30 initially [ 6 ] . During our standardized initial data analysis process and collinearity diagnosis, some variables were recoded or removed and the total number of variables (17 in the ODSF model out of a possible 25) resulted in sufficient power. Lastly, as this is the first pan-Canadian survey on this topic, various questions were newly designed for the survey. We used sensibility testing with patients and experts to gather information on face validity. CONCLUSION This cross-sectional survey involving over 1,500 individuals living with chronic pain across the 10 Canadian provinces found 17 risk factors of clinically significant decisional conflict. Most of the risk factors are associated with the decision-making process. Future shared decision-making interventions can be potentiated by targeting multiple risk factors of decisional conflict. METHODS Study design and settings We conducted a pan-Canadian cross-sectional online survey. The protocol was previously published [ 6 ] . We reported data using the Checklist for Reporting of Survey Studies [ 39 ] . We conducted the statistical analysis according to the STRengthening Analytical Thinking for Observational Studies recommendations (STRATOS) [ 40 ] . Patient involvement Three patient partners living with chronic pain participated as members of the steering committee responsible for co-developing this protocol and were recognized as co-authors. Among them, two were also rehabilitation professionals. Two patient partners brought over five years of experience in health research, while a novice partner joined with a focus on capacity building. All patient partners received training through the Strategy for Patient-Oriented Research (SPOR) SUPPORT units in Canada. They identified tasks that were most meaningful to them, and we collaboratively determined fair compensation for their contributions. Respondents We recruited a randomly selected sample from the panel of Leger Marketing ( https://leger360.com/ ). This private research analytical firm maintains a panel of 500,000 representative members of Canadian society with Internet access across the 10 Canadian provinces. The inclusion criteria were: 1) being a citizen, permanent resident or refugee living in Canada, 2) aged ≥ 18, 3) reading, writing and understanding French or English, and 4) experiencing primary chronic pain (e.g., low back pain,) or secondary chronic pain (e.g., pain from persistent inflammation) [ 41 ] . The International Association for the Study of Pain defined chronic primary pain as pain in one or more anatomical regions that persists or recurs for longer than 3 months and that cannot be better accounted for by another chronic pain condition [ 42 ] , and chronic secondary pain as chronic pain that is linked to other diseases as the underlying cause, for which pain may initially be regarded as a symptom [ 41 ] . We excluded individuals with cancer-related chronic pain due to their distinct decision-making context, which often involves critical decisions around life expectancy, palliative care, and curative treatments. This differs from the symptom management focus typical in non-cancer chronic pain. Cancer treatments, like chemotherapy and radiation, introduce unique decisional needs, while the psychological impact of a life-threatening diagnosis may alter decision-making processes, potentially affecting conflict experiences [ 43 ] . We excluded respondents with chronic cancer pain or chronic post-cancer treatment pain [ 44 ] . We employed a stratified proportional random sampling approach. First, the sample was stratified by Canadian provinces (Alberta, British Columbia, Manitoba, Newfoundland and Labrador, New Brunswick, Nova Scotia, Ontario, Prince Edward Island, Quebec, and Saskatchewan). Next, the sample was proportionally adjusted according to population size and chronic pain prevalence [ 45 , 46 ] . Leger Marketing's software was then used to randomly select participants. After each solicitation, a new random sample was generated, considering characteristics of previous participants (e.g., sex, age, ethnic and cultural backgrounds, education level) to enhance sample representativeness. Data collection The survey included a question on language preference (English or French) and three sections covering participant characteristics. The complete questionnaire comprised 50 questions and required approximately 30 minutes to complete. Since no standardized tool existed to assess the decisional needs of individuals living with chronic primary and secondary pain, we developed a custom questionnaire addressing six domains: 1) difficult decisions, 2) healthcare needs, 3) decisional conflict, 4) decision regret, 5) decisional needs, and 6) participant characteristics. The questionnaire is available in the protocol [ 6 ] . Dependent variable: Clinically significant decisional conflict We used the Decisional Conflict Scale (Statement form) to gather information on CSDC associated with the most difficult decision experienced in pain care, The Decisional Conflict Scale comprises 16 items designed to capture information about uncertainty, lack of information, unclear values, feeling unsupported, and ineffective decision-making [ 9 ] . The Decisional Conflict Scale is assessed on a 5-point Likert scale and transformed into a continuous score (0 = absence of decisional conflict and 100 = maximum decisional conflict) [ 47 ] . A score ≥ 37.5 signifies CSDC [ 48 ] . A less stringent cutoff (i.e., score ≥ 25, increasing sensitivity) has been used by some authors [ 48 – 51 ] . The Decisional Conflict Scale was validated in rheumatology [ 9 ] and other clinical settings such as primary care [ 9 ] . The scale has cross-cultural validity in Canadian French language [ 52 ] . Independent and adjustment variables We describe the variables and data collection methods in Table 1 . We divided independent variables into two categories: independent variables from the decisional needs domains of the Ottawa Decision Support Framework (ODSF) [ 20 ] , and other variables (i.e., non-ODSF variables). The ODSF contains eight decisional needs domains: 1) decisional type and timing, 2) decisional stage, 3) decisional conflict, 4) knowledge, 5) expectations, 6) values, 7) support and resources to make and implement the decision, and 8) personal and clinical needs [ 20 ] . Since our data collection was based on a past appointment, we added an adjustment variable based on respondents’ self-rated accuracy in recalling a clinical consultation. We collected this variable on a visual analog scale of 0-100, where 0 was no recall of the consultation and 100 meaning recalling this moment as if the person was still living it right now. Table 1 Independent and adjustment variables and measures. Ottawa Decision Support Framework independent variables Characteristics of the respondents Construct Measure Ref Sex at birth Self-developed question based on a systematic review [ 53 ] Gender Self-developed question driven by the classification used by federal organization (Statistics Canada) [ 54 ] Pain duration Self-developed question Quality of life Kemp Quality of Life Scale [ 55 ] Education level Self-developed question driven by the International Standard Classification of Education [ 56 ] Geographical area Postal code including a numeral zero is related to rural area. [ 57 ] Ethnic and cultural background Self-developed question driven by the classification used by federal organization (Statistics Canada) [ 58 ] Spirituality Self-developed question driven by the classification used by federal organization (Statistics Canada) [ 59 ] Marital status Self-developed question driven by the classification used by federal organization (Statistics Canada) [ 60 ] Household income Self-developed question based on a previous survey [ 61 ] Work status Self-developed question based on a previous survey [ 61 ] Decisional needs Difficult decision Self-developed question driven based on previous study and report [ 62 – 64 ] Previous knowledge Self-developed question Health literacy One item from the Brief Health Literacy Screening Tool [ 65 ] Decision self-efficacy Self-developed question Others involvement Self-developed question Assumed role Control Preferences Scale [ 23 ] Congruence between assumed and preferred role Control Preferences Scale [ 23 ] Non-ODSF independent variables Characteristics of the respondents Canadian provinces Self-developed question Satisfaction with current health state Patient Acceptable Symptom State [ 66 ] Comorbidity Self-developed question based on the chronic pain series from the Lancet journals [ 67 ] Perception of disability Self-developed question based on the IASP definition of high-impact chronic pain [ 68 ] Stress Self-developed question Number of people living at home Self-developed question based on a previous survey [ 61 ] Native language Self-developed question based on the classification used by federal organization (Statistics Canada) [ 69 ] Pain location Self-developed question based on the anatomical regions of the body Decisional needs Congruence between the chosen and preferred option Self-developed question Considered elements Self-developed question based on the practical issues to inform shared decision-making [ 70 ] Adjustment variable Accuracy of the consultation recall Self-developed question Most difficult decisions (i.e., decisional type of the ODSF) Based on Canadian’s reports [ 63 , 64 , 71 ] , study [ 62 ] and the experiences of the patient partners, we proposed 10 pre-specified most difficult decisions: 1) Should I take medication or not?, 2) Should I get surgery or not?, 3) Should I change my treatment?, 4) Should I stop my treatment?, 5) Should I change my lifestyle habits and behaviours?, 6) Should I consult a rehabilitation professional?, 7) Should I consult a complementary and alternative medicine professional?, 8) Should I consult a mental-health professional?, 9) Should I change the health care provider to manage my condition?, and 10) Should I undergo more diagnostic tests? Respondents could also specify another most difficult decision. We gathered data on the decision-making related to the selected most difficult decision. Pretesting of the survey and administration We conducted a clinical sensibility testing [ 72 , 73 ] involving our patient partners (n = 2), experts in survey methodology (n = 7), and shared decision-making experts (n = 9) to evaluate the face validity of our survey. We then performed a pilot test with 50 random respondents to evaluate feasibility, readability, and comprehensibility of our survey. Following pretesting, Leger Marketing sent email invitations to randomly selected eligible participants. Participants had access to the questionnaire for three weeks, with weekly reminders sent to those who had not yet completed it. Each participant completed the questionnaire only once. Participation was voluntary and free, with participants receiving standardized compensation from Leger Marketing: 1,200 LEO points, which could be redeemed for gift cards, prepaid VISA/Mastercard cards, or donated to charity. To minimize missing data, participants were required to answer all questions to submit the completed questionnaire. To ensure data quality, we randomized the order of certain questions within domains to minimize question order effects [ 74 ] , as well as the order of response options in lists to reduce response order bias [ 74 ] . Participants were allowed to pause and save the questionnaire at any time, enabling them to resume later and thus helping to prevent accuracy loss due to fatigue [ 75 ] or pain associated with prolonged cognitive activity or sitting [ 76 , 77 ] . We excluded questionnaires completed in less than ten minutes to avoid potential inaccuracies due to the length of the survey [ 78 ] . Quality control questions were embedded throughout the survey to identify inattentive responses; for instance, participants were instructed to select a specific option (e.g., 'I don’t know') to confirm they were reading carefully. Ethical considerations We obtained ethics approval from the Research Ethics Board of the Research Centre at the Centre Hospitalier Universitaire de Sherbrooke (project #2022–4645) and respected Canadian regulation on personal information protection. All experiments were performed in accordance with relevant guidelines and regulations and all respondents gave their informed consent. Statistical analysis We performed the statistical analyses in R (version 4.3.3, packages reported in Supplementary Methods S4 ) and were supported by biostatisticians. Sample size calculation We planned the sample size to identify risk factors of CSDC (i.e., binary logistic regression). We estimated the prevalence of CSDC at 10% [ 49 ] and planned to build an initial regression model with 30 variables [ 6 ] . According to a context-specific sample size calculation method [ 79 ] , a sample of 1,649 respondents was needed. We provided details on this sample size calculation in the protocol [ 6 ] . Data preparation We verified coverage and participation biases to determine the need of weighting across provinces using data from the 2021 census data of Statistics Canada [ 80 ] . We performed initial data analysis (i.e., metadata, data cleaning, and data screening) [ 81 ] with two independent reviewers to increase its robustness, and of missing data according to the Treatment And Reporting of Missing data in Observational Studies framework [ 82 ] . We used multiple imputation with multivariate imputation by chained equations (MICE) [ 83 – 85 ] . We opted for predictive mean matching method as imputation technique because it is an effective imputation approach for continuous, ordered categorical and dichotomous multilevel data [ 86 ] . Independent variables with collinearity were deleted of the imputation model [ 87 ] . We used all the variables (dependent and independent) of the initial model except gender, work status, number of comorbidities, and number of pain location because of their collinearity. We checked the consistency of the imputed data with data visualization. We calculated fraction of missing information (i.e., an important parameter for diagnosing the effects of data missingness [ 88 ] ) to determine the number of multiple imputation cycles. The highest fraction of missing information was 0.085 (pain duration variable) leading to six cycles of multiple imputation [ 89 ] . We report missing data and details and result of the consistency of the imputed data in Supplementary Methods S4 . Multilevel regression analysis We developed a descriptive model from our conceptual underpinning: the ODSF [ 90 ] . We aimed to minimize bias in the regression coefficients [ 91 ] . Inference should be performed in the global model due to the absence of directed acyclic graph [ 91 ] . We performed multilevel analysis to account for potential clustering effects of the Canadian provinces [ 92 ] . We qualified level-1 independent variables as fixed effect and level-2 independent variable as random effect [ 93 ] . We visually verified the model assumptions (i.e., outliers, leverage, linearity, normality of residuals, homoscedasticity) [ 94 ] . We conducted collinearity diagnosis with the variance inflation factor (VIF). Because VIF measures the degree to which collinearity among the independent variables degrades the precision of estimate coefficients [ 95 ] , we decided to use a conservative cutoff of 2.5 [ 96 ] . We removed the variable with the highest VIF and re-ran the diagnosis until we had a model with all variables below the cutoff. We provide information on recoding of variables in Supplementary Methods S4 . We built an intercept-only model to obtain the intraclass correlation coefficient (ICC) [ 92 ] . We built two multilevel binary logistic regression models with a Decisional Conflicts Scale cut-off of 37.5 including the risk factors from the ODSF model expanded with the non-ODSF variables. We performed regression analysis on each imputed dataset and pooled the results. We reported the results with odds ratio and related 95% confidence intervals. We performed a complete case sensitivity analysis of the ODSF model to test the impact of multiple imputation on the results. We conducted a multilevel linear sensitivity analysis to test the impact of the dichotomization of the outcome [ 21 , 97 ] and a multilevel binary logistic sensitivity analysis with a Decisional Conflict Scale cut-off of 25. Declarations CRediT authorship contribution statement F.N. and S.D. wrote the manuscript. All other authors reviewed and edited the manuscript. Competing interests The authors declare no competing interests. Funding This work was supported by the Canadian Institutes of Health Research (grant number 435994) and Réseau-1 Québec. Author Contribution F.N. and S.D. wrote the main manuscript text and all authors reviewed and edited the manuscript. Acknowledgement We thank Samuel Lemaire-Paquette and Catherine Allard, biostatisticians, for their support in this article. Data Availability Data are available upon request to Simon Décary. References Hylands-White, N., Duarte, R. V. & Raphael, J. H. An overview of treatment approaches for chronic pain management. 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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-5417847","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":383926934,"identity":"4deb9870-7079-4ab5-93d7-1f92d4baa928","order_by":0,"name":"Florian Naye","email":"","orcid":"","institution":"Université de Sherbrooke, Faculty of Medicine and Health Sciences, School of Rehabilitation","correspondingAuthor":false,"prefix":"","firstName":"Florian","middleName":"","lastName":"Naye","suffix":""},{"id":383926936,"identity":"f0f4dacc-7bb7-4939-b555-17f5dd3a9e29","order_by":1,"name":"Yannick Tousignant-Laflamme","email":"","orcid":"","institution":"Université de Sherbrooke, Faculty of Medicine and Health Sciences, School of Rehabilitation","correspondingAuthor":false,"prefix":"","firstName":"Yannick","middleName":"","lastName":"Tousignant-Laflamme","suffix":""},{"id":383926937,"identity":"84c81644-d313-439b-81fa-7ddb32f603da","order_by":2,"name":"Maxime Sasseville","email":"","orcid":"","institution":"VITAM Research Center for Sustainable Health, Quebec Integrated University Health and Social Services Center (CIUSSS de la Capitale-Nationale)","correspondingAuthor":false,"prefix":"","firstName":"Maxime","middleName":"","lastName":"Sasseville","suffix":""},{"id":383926938,"identity":"2a891acf-ec00-4632-95ff-fd9104b60789","order_by":3,"name":"Chloé Cachinho","email":"","orcid":"","institution":"Université de Sherbrooke, Faculty of Medicine and Health Sciences, School of Rehabilitation","correspondingAuthor":false,"prefix":"","firstName":"Chloé","middleName":"","lastName":"Cachinho","suffix":""},{"id":383926940,"identity":"9094da66-3625-4650-90e3-df0c50922df6","order_by":4,"name":"Thomas Gérard","email":"","orcid":"","institution":"Université de Sherbrooke, Faculty of Medicine and Health Sciences, School of Rehabilitation","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Gérard","suffix":""},{"id":383926941,"identity":"d2960006-e0a5-4977-b400-070133491fc0","order_by":5,"name":"Karine Toupin-April","email":"","orcid":"","institution":"University of Ottawa, Faculty of Health Sciences, School of Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Karine","middleName":"","lastName":"Toupin-April","suffix":""},{"id":383926942,"identity":"f3c1e02b-0f17-4503-ba2d-798b361e4785","order_by":6,"name":"Olivia Dubois","email":"","orcid":"","institution":"Université de Sherbrooke, Faculty of Medicine and Health Sciences, School of Rehabilitation","correspondingAuthor":false,"prefix":"","firstName":"Olivia","middleName":"","lastName":"Dubois","suffix":""},{"id":383926943,"identity":"dea63442-fa63-4d65-8100-97ca285af662","order_by":7,"name":"Jean-Sébastien Paquette","email":"","orcid":"","institution":"VITAM Research Center for Sustainable Health, Quebec Integrated University Health and Social Services Center (CIUSSS de la Capitale-Nationale)","correspondingAuthor":false,"prefix":"","firstName":"Jean-Sébastien","middleName":"","lastName":"Paquette","suffix":""},{"id":383926944,"identity":"3e0a1a75-6cb8-4c79-9f57-d59ffe92c2fa","order_by":8,"name":"Annie LeBlanc","email":"","orcid":"","institution":"VITAM Research Center for Sustainable Health, Quebec Integrated University Health and Social Services Center (CIUSSS de la Capitale-Nationale)","correspondingAuthor":false,"prefix":"","firstName":"Annie","middleName":"","lastName":"LeBlanc","suffix":""},{"id":383926945,"identity":"f74d6ce5-7d38-41b4-b05a-5d8f530bc33b","order_by":9,"name":"Isabelle Gaboury","email":"","orcid":"","institution":"Université de Sherbrooke, Faculty of Medicine and Health Sciences, Department of Family Medicine and Emergency Medicine","correspondingAuthor":false,"prefix":"","firstName":"Isabelle","middleName":"","lastName":"Gaboury","suffix":""},{"id":383926946,"identity":"76a0b798-c9cf-416e-8673-5e7ab5ef9b40","order_by":10,"name":"Marie-Eve Poitras","email":"","orcid":"","institution":"Université de Sherbrooke, Faculty of Medicine and Health Sciences, School of Rehabilitation","correspondingAuthor":false,"prefix":"","firstName":"Marie-Eve","middleName":"","lastName":"Poitras","suffix":""},{"id":383926948,"identity":"636c8f7d-964b-4bb7-baab-16cf2c25c82b","order_by":11,"name":"Linda C. Li","email":"","orcid":"","institution":"University of British Columbia, Department of Physical Therapy","correspondingAuthor":false,"prefix":"","firstName":"Linda","middleName":"C.","lastName":"Li","suffix":""},{"id":383926949,"identity":"596d2445-e520-4c0c-b182-6de83fb97956","order_by":12,"name":"Alison Hoens","email":"","orcid":"","institution":"University of British Columbia, Department of Physical Therapy","correspondingAuthor":false,"prefix":"","firstName":"Alison","middleName":"","lastName":"Hoens","suffix":""},{"id":383926950,"identity":"2ddb7aac-6741-450a-8d4a-fd4042baf3e4","order_by":13,"name":"Marie-Dominique Poirier","email":"","orcid":"","institution":"Université de Sherbrooke, Faculty of Medicine and Health Sciences, School of Rehabilitation","correspondingAuthor":false,"prefix":"","firstName":"Marie-Dominique","middleName":"","lastName":"Poirier","suffix":""},{"id":383926951,"identity":"74098917-1450-4d7d-88a2-9e9b70f1967a","order_by":14,"name":"France Légaré","email":"","orcid":"","institution":"Université Laval, Canada Research Chair in Shared Decision Making and Knowledge Translation","correspondingAuthor":false,"prefix":"","firstName":"France","middleName":"","lastName":"Légaré","suffix":""},{"id":383926954,"identity":"2f9f9ee9-3d0c-4253-97ef-217ea76974f3","order_by":15,"name":"Simon Décary","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIie2PMUvDQBiGv3CSDF7MenLQ/IWUgFCK/yVHIFliKQiSKS0I16WY9fwXEcFV4UCXxLmjUMh8Y0fbRsTCFTIWvGf4OD7u4XtfAIPhFEF/JiiACADvnlE/xRK9lAMR91H8xTlbT2E88BZNS695MfFo865UngzAkV86JZDucyggDUmdXtEbLm8vHybxo6izEHASaBXkvlAMklWQ2FvljVU1DpHLczYnoFX8+06ZVV5r0xEv2OuPMpsTR2nLyE6JArK9YnHEKrxXsggI1gfbdcFBOhSrFo2Wn5KJ+iK2RJ0MOc6m2mBl87TG+dj3ysRabe4KVi5dCSqPfc/5qLTBulv7eUYOlvbx/78gfVuDwWD493wDqSNUS/6Z3SwAAAAASUVORK5CYII=","orcid":"","institution":"Université de Sherbrooke, Faculty of Medicine and Health Sciences, School of Rehabilitation","correspondingAuthor":true,"prefix":"","firstName":"Simon","middleName":"","lastName":"Décary","suffix":""}],"badges":[],"createdAt":"2024-11-08 16:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5417847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5417847/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-28803-y","type":"published","date":"2025-11-22T15:57:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71795140,"identity":"e6d1ab42-61af-4cfc-9bca-8fe8d099b951","added_by":"auto","created_at":"2024-12-18 15:57:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244820,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the multilevel binary logistic regression model based on the decisional needs domains of the Ottawa Decision Support Framework.\u003c/p\u003e\n\u003cp\u003e* Statistically significant\u003c/p\u003e\n\u003cp\u003e† Transformed by per unit Standard Deviation (i.e., SD\u003csub\u003eage\u003c/sub\u003e = 16.36 and SD\u003csub\u003epain duration \u003c/sub\u003e= 113.95)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCAD\u003c/strong\u003e\u003c/em\u003e: Canadian Dollar\u003c/p\u003e","description":"","filename":"NAYEDecisionalConflictFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5417847/v1/a8db8b5dcbd50c53349bfa0a.jpg"},{"id":96651110,"identity":"331dfbd8-91bb-481a-adce-801a175aa1bb","added_by":"auto","created_at":"2025-11-24 16:13:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1585617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5417847/v1/4d3a1ac2-158d-48f8-bcfe-88f27bf82ddb.pdf"},{"id":71794032,"identity":"94c00c1d-a71f-471b-9706-3ea2e3bea625","added_by":"auto","created_at":"2024-12-18 15:49:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":841482,"visible":true,"origin":"","legend":"","description":"","filename":"NAYEDecisionalConflictSupplementaryInformationFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5417847/v1/4388471b3c50689a28bb1c8c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Factors of Decisional Conflict in People Living with Chronic Pain: a pan-Canadian survey.","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDecision-making in pain care is characterized by uncertainty. People living with chronic pain face difficult decisions with multiple management approaches, often without a best option\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The decision-making requires trade-offs, affected by the uncertainty of evidence and outcomes\u003csup\u003e[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Uncertainty in healthcare can lead to decisional conflict\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Recently, our team conducted a survey across all Canadian provinces to describe decision-making and decision-making needs in pain care\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. We found that a third of people living with chronic in Canada experienced decisional conflict following a difficult decision for their condition\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDecisional conflict is defined as \u0026ldquo;a state of uncertainty about the course of action to take\u0026rdquo;\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Decisional conflict arises when there is a gap between the support and information people need to make a decision and what they actually receive from healthcare providers\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Decisional conflict is mainly measured in research with the Decisional Conflict Scale\u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Above a certain score threshold on Decisional Conflict Scale, people are experiencing clinically significant decisional conflict (CSDC)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e which is associated with decisional delay, departure from chosen option, decision regret, nervousness, and intention to sue physicians in cases of harms from chose option\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Reducing CSDC benefits both people living with chronic pain and clinicians, leading to improved clinical outcomes\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eShared decision-making interventions can reduce decisional conflict\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Authors of an updated Cochrane review from 2024 highlighted that patient decision aids improve the decision-making process and reduce decisional conflict across various conditions\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Patient decision aids effectiveness is inconsistent in the context of chronic musculoskeletal pain\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. People living with chronic pain exhibit highly heterogeneous profiles\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The lack of understanding of individual and decision-making characteristics - or risk factors - leading to CSDC results in shared decision-making interventions not meeting people\u0026rsquo;s decisional needs. Identifying and targeting risk factors of CSDC will improve the development of comprehensive shared decision-making interventions that optimize decision-making processes and outcomes.\u003c/p\u003e \u003cp\u003eThe aim of this study was to identify risk factors of CSDC among people living with chronic pain.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cem\u003eSurvey administration and attrition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe randomly invited 31,545 members within Leger Marketing\u0026rsquo;s panel. The invitation view rate was 18.9% (5,949/3,1545). The participation rate was 44.8% (2,666/5,949) and the completion rate was 61.9% (1,649/2,666) \u0026ndash; 1,649 people living with chronic pain completed the survey in four weeks from August 31, 2022 to September 28, 2022. We had no a priori information on the percentage of people living with chronic pain who did not experience difficult decisions (i.e., attrition). After data collection, 276 respondents (16.7%) did not report a most difficult decision leading to no information on decisional conflict \u0026ndash; 1,373 reported a Decisional Conflict Scale score.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCharacteristics of the respondents\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe describe the respondents\u0026rsquo; characteristics in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e. Of the 1,373 respondents who experienced a most difficult decision, mean age was 52 (SD=16.4). Half of respondents were men (49.5%), most lived in urban areas (88.2%), and pain duration ranged from 3 months to 59 years. Two thirds (68.1%) perceived assuming a collaborative role during the decision-making process and 71% reported having difficulty understanding what healthcare providers said about their medical condition. One third (33.7%) of the sample experienced CSDC (Decisional Conflict Scale score \u0026ge;37.5).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMultilevel regression analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrom the intercept-only model, we calculated the mean ICC (i.e, intraclass correlation coefficient) from the imputed datasets and obtained 0.002 indicating that a small proportion of the total variance was explained by the provinces.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRisk factors of CSDC (i.e., Decisional Conflict Scale score \u0026ge;37.5))\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe present the results of the Ottawa Decision Support Framework (ODSF) model (hereafter ODSF model) in \u003cstrong\u003eFigure 1\u003c/strong\u003e. We report details on the results for the ODSF model variables and the results of non-ODSF model variables in \u003cstrong\u003eSupplementary Table S2.\u003c/strong\u003e Out of 56 independent variables, we found 17 risk factors of CSDC of which seven were associated with an increased risk of CSDC and 10 were associated with a reduced risk. Half (47.1%) of the significant risk factors were ODSF-related variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCAD\u003c/em\u003e\u003c/strong\u003e: Canadian Dollar\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRisk factors associated with an increased risk of CSDC\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrom the ODSF model, we found two variables statistically associated with increasing risk of CSDC: 1- having difficulty understanding what healthcare providers said about their condition compared to never having difficulty (\u0026ldquo;sometimes\u0026rdquo;: OR=2.43, 95%CI [1.65;3.58], \u0026ldquo;often\u0026rdquo;: OR=2.23, 95%CI [1.34;3.69]), and 2- no prior knowledge (OR=2.03, 95%CI [1.27;3.25]) or prior knowledge of certain options (OR=1.47, 95%CI [1.00;2.14]) compared to prior knowledge of all options.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom the non-ODSF model, five variables were statistically associated with increasing risk of CSDC: 1- no congruence between the chosen and preferred option (OR=3.00, 95%CI [1.91;4.72]) or not having a preferred option compared to congruence (OR=1.97, 95%CI [1.31;2.98]), 2- being unsatisfied by their current health state (OR=1.99, 95%CI [1.39;2.84]) compared to satisfied, 3- first learned language different to English (OR=1.90, 95%CI [1.10;3.26]), 4- presence of pelvic pain (OR=1.72, 95%CI [1.06;2.78]), and 5- presence of low limb pain (OR=1.51, 95%CI [1.06;2.13]).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRisk factors associated with a reduced risk of CSDC\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrom the ODSF model, six variables were associated with reducing risk of CSDC: 1- decision about consulting a mental health professional (OR=0.42, 95%CI [0.18;0.95]) compared to decision about taking medication, 2- perception of assuming a collaborative role (OR=0.46, 95%CI [0.30;0.69]) compared to active role, 3- congruence between assumed and preferred role (OR=0.57, 95%CI [0.43;0.77]) compared to non-congruence, 4- higher decision self-efficacy (OR=0.65, 95%CI [0.59;0.70]), 5- higher quality of life (OR=0.71, 95%CI [0.63;0.81]), and 6- higher age (OR=0.80, 95%CI [0.68;0.94]).\u003c/p\u003e\n\u003cp\u003eFrom the non-ODSF model, four variables were associated with reducing risk of CSDC: 1- presence of alcohol-related disorders (OR=0.41, 95%CI [0.17;0.99]), 2- perception of not being stressed during the consultation (OR=0.42, 95%CI [0.29;0.59]), 3- considering the consequences of the available options on work ability during the decision-making process (OR=0.44, 95%CI [0.29;0.67]), and 4- presence of hypertension (OR=0.63, 95%CI [0.41;0.96]).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSensitivity analysis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe found similar results when comparing the complete case analysis with the imputed analysis. The main difference emerging from the multilevel linear regression analysis concerned health literacy and the contribution of always having difficulty to understand what healthcare providers said about their condition (\u0026szlig;=-7.79). We found the same result (OR=0.10, 95%CI [0.03;0.36]) with the multilevel binary logistic regression analysis with a DCS cut-off of 25. \u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e reported the results of all sensitivity analysis.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis pan-Canadian survey identified 17 risk factors of CSDC associated with difficult decisions for pain care. Half of the significant risk factors were ODSF-related variables. The risk factors with the highest odd ratios concerned congruence between the chosen and preferred role, having difficulty to understand what the healthcare providers said about their condition, and no prior knowledge of the available options. Based on these discoveries, we make four observations to improve shared decision-making interventions in pain care.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFuture shared decision-making interventions will need to target the decision-making process.\u003c/b\u003e Eight out of 17 risk factors are mainly related to the decision-making process from both the ODSF and non-ODSF models. For example, our data indicates that not reaching congruence between the chosen and preferred option in a clinical consultation is associated with twice the risk of a person experiencing CSDC. The importance of the decision-making characteristics is consistent with a recent systematic review\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Becerra-P\u0026eacute;rez et al. reviewed the risk factors of decision regret, a consequence of decisional conflict, in 59 studies across multiple clinical contexts (e.g., oncology, family practice)\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Consistent with our results, the authors found that numerous risk factors of decisional regret were related with decision-making process such as a person\u0026rsquo;s role in making decisions\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. From our finding (i.e., half of the significant risk factors were ODSF-related variables) and a previous overview of systematic reviews\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, we argue that researchers should use decisional needs assessment based on the ODSF prior to developing future shared decision-making interventions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFuture interventions will need to target role matching.\u003c/b\u003e We found that congruence between the assumed and preferred role could reduce the risk of CSDC. We see this protective factor as an easy target for improvement, as our previous result found that only 50% of people living with chronic pain in Canada perceived assuming a role that is congruent with their preferred one\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Targeting role matching could also have positive impacts, as it was showed that congruence between the assumed and preferred role can improve quality of life (i.e., one risk factor of CSDC)\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Matching collaborative role could also help to address another risk factor of CSDC: the congruence between the chosen and preferred option. Because collaborative role assumed deliberation on the preferences of a person and a clinician, the chosen option had greater chance to be aligned with the preferred one\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Addressing role matching during a consultation can easily be done with one validated question such as our survey did with the Control Preferences Scale\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. We argue that clinicians could meet decisional needs for people living with chronic pain and reduce decisional conflict by aligning their assumed and preferred roles early in care.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFuture interventions will adapt to the person\u0026rsquo;s health literacy.\u003c/b\u003e Not understanding what the clinician said in a consultation is associated with an increased risk of CSDC. Targeting health literacy is an expanding field within the shared decision-making literature\u003csup\u003e[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Low health literacy may lead to potential cognitive biases impacting the shared decision-making process\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Because low health literacy is prevalent\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, shared decision-making interventions should integrate people with low health literacy in their co-development\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Including people with low literacy is an ethical foundation for shared decision-making as evidence showed that average readability of online support resources is higher than recommended for patient literature\u003csup\u003e[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In a recent example in rheumatoid arthritis, authors built a co-design team including individuals with expertise in health literacy and patients with a large range of health literacy\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. The decision aid they produced was tested in a 3-arm pilot study on 166 participants and reported promising results on improving decisional conflict\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Our sensitivity analyses suggest that systematic difficulty to understand what healthcare providers said is associated with a reduced risk of CSDC. This finding warrants further investigation in future health literacy studies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFuture interventions will need to be designed longitudinally.\u003c/b\u003e Most interventions such as patient decision aids are used at a single time point (i.e., during the consultation) and that may explain why shared decision-making interventions in pain care have not reached their full potential. We found that having no prior knowledge of available options is associated with an increased risk of CSDC. In our survey, half of respondent indicated wanting to acquire knowledge of these options with their healthcare providers. These results outline the importance of education on available options to prepare a consultation as pain care involve numerous options that cannot be tackled in one \u0026ldquo;15-min\u0026rdquo; consultation. The Juvenile idiopathic arthritis Option MAP\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e aims to solve this issue with a decision aid that proposes pre-consultation stages with patient-reported outcomes, value clarification, and education on available options\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. The person can select option preferences and report barriers to implementing these options. The summary can be used during the consultation to support the information exchange and ease the decision-making process. As pain care is already a complex endeavour\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, artificial intelligence tools could support decision aids that would aggregate demographic characteristics, patient-reported outcomes, health system utilization, and comorbidities to the benefit of increasing engagement and personalization of pain care\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eA strength of our study is the limited risk of coverage and participation biases due to our stratified proportional random sampling improving the representativeness of the sample. A second strength is the use of strategies to reduce the incentive bias. A limitation of our study is that Canadians from Territories are not represented in our survey as well as the 6% of the Canadian population not having Internet access at home (Canadian census\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e). Future studies will need to replicate our findings in Indigenous people and other hardly reached population who traditionally have poor access to our Canadian health systems. Another limitation is the retrospective nature of the questions requiring recall of a clinical consultation. We minimized this recall bias by adjusting the models with respondent\u0026rsquo;s perception of the recall accuracy. Concerning our sample size, not all 1,649 respondents reported a Decisional Conflict Scale score. The attrition did not affect the representativeness of the sample (\u003cb\u003eSupplementary Table S3\u003c/b\u003e). A post hoc calculation adjusted with the measured prevalence of CSDC revealed sufficient statistical power to build our initial regression model including 25 independent variables instead of 30 initially\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. During our standardized initial data analysis process and collinearity diagnosis, some variables were recoded or removed and the total number of variables (17 in the ODSF model out of a possible 25) resulted in sufficient power. Lastly, as this is the first pan-Canadian survey on this topic, various questions were newly designed for the survey. We used sensibility testing with patients and experts to gather information on face validity.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis cross-sectional survey involving over 1,500 individuals living with chronic pain across the 10 Canadian provinces found 17 risk factors of clinically significant decisional conflict. Most of the risk factors are associated with the decision-making process. Future shared decision-making interventions can be potentiated by targeting multiple risk factors of decisional conflict.\u003c/p\u003e "},{"header":"METHODS","content":"\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003eStudy design and settings\u003c/h2\u003e\n \u003cp\u003eWe conducted a pan-Canadian cross-sectional online survey. The protocol was previously published\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. We reported data using the Checklist for Reporting of Survey Studies\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. We conducted the statistical analysis according to the STRengthening Analytical Thinking for Observational Studies recommendations (STRATOS)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePatient involvement\u003c/h2\u003e\n \u003cp\u003eThree patient partners living with chronic pain participated as members of the steering committee responsible for co-developing this protocol and were recognized as co-authors. Among them, two were also rehabilitation professionals. Two patient partners brought over five years of experience in health research, while a novice partner joined with a focus on capacity building. All patient partners received training through the Strategy for Patient-Oriented Research (SPOR) SUPPORT units in Canada. They identified tasks that were most meaningful to them, and we collaboratively determined fair compensation for their contributions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eRespondents\u003c/h2\u003e\n \u003cp\u003eWe recruited a randomly selected sample from the panel of Leger Marketing (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://leger360.com/\u003c/span\u003e\u003c/span\u003e). This private research analytical firm maintains a panel of 500,000 representative members of Canadian society with Internet access across the 10 Canadian provinces. The inclusion criteria were: 1) being a citizen, permanent resident or refugee living in Canada, 2) aged\u0026thinsp;\u0026ge;\u0026thinsp;18, 3) reading, writing and understanding French or English, and 4) experiencing primary chronic pain (e.g., low back pain,) or secondary chronic pain (e.g., pain from persistent inflammation)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The International Association for the Study of Pain defined chronic primary pain as pain in one or more anatomical regions that persists or recurs for longer than 3 months and that cannot be better accounted for by another chronic pain condition\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, and chronic secondary pain as chronic pain that is linked to other diseases as the underlying cause, for which pain may initially be regarded as a symptom\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. We excluded individuals with cancer-related chronic pain due to their distinct decision-making context, which often involves critical decisions around life expectancy, palliative care, and curative treatments. This differs from the symptom management focus typical in non-cancer chronic pain. Cancer treatments, like chemotherapy and radiation, introduce unique decisional needs, while the psychological impact of a life-threatening diagnosis may alter decision-making processes, potentially affecting conflict experiences\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. We excluded respondents with chronic cancer pain or chronic post-cancer treatment pain\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. We employed a stratified proportional random sampling approach. First, the sample was stratified by Canadian provinces (Alberta, British Columbia, Manitoba, Newfoundland and Labrador, New Brunswick, Nova Scotia, Ontario, Prince Edward Island, Quebec, and Saskatchewan). Next, the sample was proportionally adjusted according to population size and chronic pain prevalence\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Leger Marketing\u0026apos;s software was then used to randomly select participants. After each solicitation, a new random sample was generated, considering characteristics of previous participants (e.g., sex, age, ethnic and cultural backgrounds, education level) to enhance sample representativeness.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eData collection\u003c/h2\u003e\n \u003cp\u003eThe survey included a question on language preference (English or French) and three sections covering participant characteristics. The complete questionnaire comprised 50 questions and required approximately 30 minutes to complete. Since no standardized tool existed to assess the decisional needs of individuals living with chronic primary and secondary pain, we developed a custom questionnaire addressing six domains: 1) difficult decisions, 2) healthcare needs, 3) decisional conflict, 4) decision regret, 5) decisional needs, and 6) participant characteristics. The questionnaire is available in the protocol\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eDependent variable: Clinically significant decisional conflict\u003c/h2\u003e\n \u003cp\u003eWe used the Decisional Conflict Scale (Statement form) to gather information on CSDC associated with the most difficult decision experienced in pain care, The Decisional Conflict Scale comprises 16 items designed to capture information about uncertainty, lack of information, unclear values, feeling unsupported, and ineffective decision-making\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The Decisional Conflict Scale is assessed on a 5-point Likert scale and transformed into a continuous score (0\u0026thinsp;=\u0026thinsp;absence of decisional conflict and 100\u0026thinsp;=\u0026thinsp;maximum decisional conflict)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. A score\u0026thinsp;\u0026ge;\u0026thinsp;37.5 signifies CSDC\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. A less stringent cutoff (i.e., score\u0026thinsp;\u0026ge;\u0026thinsp;25, increasing sensitivity) has been used by some authors\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. The Decisional Conflict Scale was validated in rheumatology\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e and other clinical settings such as primary care\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The scale has cross-cultural validity in Canadian French language\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eIndependent and adjustment variables\u003c/h2\u003e\n \u003cp\u003eWe describe the variables and data collection methods in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. We divided independent variables into two categories: independent variables from the decisional needs domains of the Ottawa Decision Support Framework (ODSF)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, and other variables (i.e., non-ODSF variables). The ODSF contains eight decisional needs domains: 1) decisional type and timing, 2) decisional stage, 3) decisional conflict, 4) knowledge, 5) expectations, 6) values, 7) support and resources to make and implement the decision, and 8) personal and clinical needs\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Since our data collection was based on a past appointment, we added an adjustment variable based on respondents\u0026rsquo; self-rated accuracy in recalling a clinical consultation. We collected this variable on a visual analog scale of 0-100, where 0 was no recall of the consultation and 100 meaning recalling this moment as if the person was still living it right now.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIndependent and adjustment variables and measures.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eOttawa Decision Support Framework independent variables\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCharacteristics of the respondents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeasure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex at birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question based on a systematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question driven by the classification used by federal organization (Statistics Canada)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuality of life\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKemp Quality of Life Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question driven by the International Standard Classification of Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeographical area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePostal code including a numeral zero is related to rural area.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthnic and cultural background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question driven by the classification used by federal organization (Statistics Canada)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpirituality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question driven by the classification used by federal organization (Statistics Canada)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question driven by the classification used by federal organization (Statistics Canada)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question based on a previous survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWork status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question based on a previous survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDecisional needs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifficult decision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question driven based on previous study and report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevious knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne item from the Brief Health Literacy Screening Tool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecision self-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers involvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssumed role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl Preferences Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCongruence between assumed and preferred role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl Preferences Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-ODSF independent variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCharacteristics of the respondents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanadian provinces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatisfaction with current health state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient Acceptable Symptom State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question based on the chronic pain series from the Lancet journals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerception of disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question based on the IASP definition of high-impact chronic pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of people living at home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question based on a previous survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNative language\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question based on the classification used by federal organization (Statistics Canada)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question based on the anatomical regions of the body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDecisional needs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCongruence between the chosen and preferred option\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsidered elements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question based on the practical issues to inform shared decision-making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjustment variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccuracy of the consultation recall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-developed question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eMost difficult decisions (i.e., decisional type of the ODSF)\u003c/h2\u003e\n \u003cp\u003eBased on Canadian\u0026rsquo;s reports\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/sup\u003e, study\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e and the experiences of the patient partners, we proposed 10 pre-specified most difficult decisions: 1) Should I take medication or not?, 2) Should I get surgery or not?, 3) Should I change my treatment?, 4) Should I stop my treatment?, 5) Should I change my lifestyle habits and behaviours?, 6) Should I consult a rehabilitation professional?, 7) Should I consult a complementary and alternative medicine professional?, 8) Should I consult a mental-health professional?, 9) Should I change the health care provider to manage my condition?, and 10) Should I undergo more diagnostic tests? Respondents could also specify another most difficult decision. We gathered data on the decision-making related to the selected most difficult decision.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003ePretesting of the survey and administration\u003c/h2\u003e\n \u003cp\u003eWe conducted a clinical sensibility testing\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/sup\u003e involving our patient partners (n\u0026thinsp;=\u0026thinsp;2), experts in survey methodology (n\u0026thinsp;=\u0026thinsp;7), and shared decision-making experts (n\u0026thinsp;=\u0026thinsp;9) to evaluate the face validity of our survey. We then performed a pilot test with 50 random respondents to evaluate feasibility, readability, and comprehensibility of our survey. Following pretesting, Leger Marketing sent email invitations to randomly selected eligible participants. Participants had access to the questionnaire for three weeks, with weekly reminders sent to those who had not yet completed it. Each participant completed the questionnaire only once. Participation was voluntary and free, with participants receiving standardized compensation from Leger Marketing: 1,200 LEO points, which could be redeemed for gift cards, prepaid VISA/Mastercard cards, or donated to charity. To minimize missing data, participants were required to answer all questions to submit the completed questionnaire.\u003c/p\u003e\n \u003cp\u003eTo ensure data quality, we randomized the order of certain questions within domains to minimize question order effects\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/sup\u003e, as well as the order of response options in lists to reduce response order bias\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/sup\u003e. Participants were allowed to pause and save the questionnaire at any time, enabling them to resume later and thus helping to prevent accuracy loss due to fatigue\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/sup\u003e or pain associated with prolonged cognitive activity or sitting\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/sup\u003e. We excluded questionnaires completed in less than ten minutes to avoid potential inaccuracies due to the length of the survey\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/sup\u003e. Quality control questions were embedded throughout the survey to identify inattentive responses; for instance, participants were instructed to select a specific option (e.g., \u0026apos;I don\u0026rsquo;t know\u0026apos;) to confirm they were reading carefully.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eEthical considerations\u003c/h2\u003e\n \u003cp\u003eWe obtained ethics approval from the Research Ethics Board of the Research Centre at the Centre Hospitalier Universitaire de Sherbrooke (project #2022\u0026ndash;4645) and respected Canadian regulation on personal information protection. All experiments were performed in accordance with relevant guidelines and regulations and all respondents gave their informed consent.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eWe performed the statistical analyses in R (version 4.3.3, packages reported in \u003cstrong\u003eSupplementary Methods S4\u003c/strong\u003e) and were supported by biostatisticians.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eSample size calculation\u003c/h2\u003e\n \u003cp\u003eWe planned the sample size to identify risk factors of CSDC (i.e., binary logistic regression). We estimated the prevalence of CSDC at 10%\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e and planned to build an initial regression model with 30 variables\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. According to a context-specific sample size calculation method\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/sup\u003e, a sample of 1,649 respondents was needed. We provided details on this sample size calculation in the protocol\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003eData preparation\u003c/h2\u003e\n \u003cp\u003eWe verified coverage and participation biases to determine the need of weighting across provinces using data from the 2021 census data of Statistics Canada\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/sup\u003e. We performed initial data analysis (i.e., metadata, data cleaning, and data screening)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e]\u003c/sup\u003e with two independent reviewers to increase its robustness, and of missing data according to the Treatment And Reporting of Missing data in Observational Studies framework\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/sup\u003e. We used multiple imputation with multivariate imputation by chained equations (MICE)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e]\u003c/sup\u003e. We opted for predictive mean matching method as imputation technique because it is an effective imputation approach for continuous, ordered categorical and dichotomous multilevel data\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e]\u003c/sup\u003e. Independent variables with collinearity were deleted of the imputation model\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e]\u003c/sup\u003e. We used all the variables (dependent and independent) of the initial model except gender, work status, number of comorbidities, and number of pain location because of their collinearity. We checked the consistency of the imputed data with data visualization. We calculated fraction of missing information (i.e., an important parameter for diagnosing the effects of data missingness\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e]\u003c/sup\u003e) to determine the number of multiple imputation cycles. The highest fraction of missing information was 0.085 (pain duration variable) leading to six cycles of multiple imputation\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eWe report missing data and details and result of the consistency of the imputed data in \u003cstrong\u003eSupplementary Methods S4\u003c/strong\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003eMultilevel regression analysis\u003c/h2\u003e\n \u003cp\u003eWe developed a descriptive model from our conceptual underpinning: the ODSF\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e]\u003c/sup\u003e. We aimed to minimize bias in the regression coefficients\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e91\u003c/span\u003e]\u003c/sup\u003e. Inference should be performed in the global model due to the absence of directed acyclic graph\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e91\u003c/span\u003e]\u003c/sup\u003e. We performed multilevel analysis to account for potential clustering effects of the Canadian provinces\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e]\u003c/sup\u003e. We qualified level-1 independent variables as fixed effect and level-2 independent variable as random effect\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e]\u003c/sup\u003e. We visually verified the model assumptions (i.e., outliers, leverage, linearity, normality of residuals, homoscedasticity)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e]\u003c/sup\u003e. We conducted collinearity diagnosis with the variance inflation factor (VIF). Because VIF measures the degree to which collinearity among the independent variables degrades the precision of estimate coefficients\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e95\u003c/span\u003e]\u003c/sup\u003e, we decided to use a conservative cutoff of 2.5\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e96\u003c/span\u003e]\u003c/sup\u003e. We removed the variable with the highest VIF and re-ran the diagnosis until we had a model with all variables below the cutoff.\u003c/p\u003e\n \u003cp\u003eWe provide information on recoding of variables in \u003cstrong\u003eSupplementary Methods S4\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eWe built an intercept-only model to obtain the intraclass correlation coefficient (ICC)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e]\u003c/sup\u003e. We built two multilevel binary logistic regression models with a Decisional Conflicts Scale cut-off of 37.5 including the risk factors from the ODSF model expanded with the non-ODSF variables. We performed regression analysis on each imputed dataset and pooled the results. We reported the results with odds ratio and related 95% confidence intervals. We performed a complete case sensitivity analysis of the ODSF model to test the impact of multiple imputation on the results. We conducted a multilevel linear sensitivity analysis to test the impact of the dichotomization of the outcome\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e97\u003c/span\u003e]\u003c/sup\u003e and a multilevel binary logistic sensitivity analysis with a Decisional Conflict Scale cut-off of 25.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCRediT authorship contribution statement\u003c/h2\u003e\n\u003cp\u003eF.N. and S.D. wrote the manuscript. All other authors reviewed and edited the manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Canadian Institutes of Health Research (grant number 435994) and R\u0026eacute;seau-1 Qu\u0026eacute;bec.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eF.N. and S.D. wrote the main manuscript text and all authors reviewed and edited the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe thank Samuel Lemaire-Paquette and Catherine Allard, biostatisticians, for their support in this article.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData are available upon request to Simon D\u0026eacute;cary.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHylands-White, N., Duarte, R. 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Shared decision-making interventions can help meet these needs; however, their effectiveness is inconsistent in the context of chronic pain. This study aimed to identify the decisional needs influencing decisional conflict among adults with chronic pain in Canada, to guide the development of more comprehensive interventions. In this pan-Canadian online survey, we measured decisional conflict related to the most difficult decision using the Decisional Conflict Scale (\u0026ge;\u0026thinsp;37.5 indicating clinically significant conflict) and assessed decisional needs based on the Ottawa Decision Support Framework. Of the 1,649 participants, 1,373 reported a Decisional Conflict Scale score. The mean age was 52 (SD\u0026thinsp;=\u0026thinsp;16.4), with half of respondents being men (49.5%) and pain duration ranging from 3 months to 59 years. One-third (33.7%) experienced clinically significant decisional conflict. Seventeen risk factors were identified, including difficulty understanding healthcare information (OR\u0026thinsp;=\u0026thinsp;2.43) and lack of prior knowledge of available options (OR\u0026thinsp;=\u0026thinsp;2.03), while role congruence in decision-making was associated with reduced conflict (OR\u0026thinsp;=\u0026thinsp;0.57). Future SDM interventions could be enhanced by targeting multiple risk factors of decisional conflict.\u003c/p\u003e","manuscriptTitle":"Risk Factors of Decisional Conflict in People Living with Chronic Pain: a pan-Canadian survey.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 15:49:39","doi":"10.21203/rs.3.rs-5417847/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T13:16:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T18:51:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127593205507896649878374609462602936123","date":"2025-10-02T13:56:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238712793853683143135036805492664556361","date":"2025-09-10T21:16:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76143830868133586667306416997322623437","date":"2025-08-01T06:55:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-02T11:35:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32568031958033749216866853457878318286","date":"2025-07-01T21:38:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122720149509857878492067267854368692854","date":"2025-03-05T17:49:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-22T10:49:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-17T01:39:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-18T12:59:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-15T14:19:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-11-08T16:15:36+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":"bc54f965-6b1d-4b4e-a76a-247199f2f343","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40900830,"name":"Health sciences/Health care/Health services"},{"id":40900831,"name":"Health sciences/Health care/Patient education"}],"tags":[],"updatedAt":"2025-11-24T16:12:03+00:00","versionOfRecord":{"articleIdentity":"rs-5417847","link":"https://doi.org/10.1038/s41598-025-28803-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-22 15:57:38","publishedOnDateReadable":"November 22nd, 2025"},"versionCreatedAt":"2024-12-18 15:49:39","video":"","vorDoi":"10.1038/s41598-025-28803-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-28803-y","workflowStages":[]},"version":"v1","identity":"rs-5417847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5417847","identity":"rs-5417847","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}