People living with chronic pain face multiple difficult decisions leading to high level of decisional conflict
Bibliographic record
Abstract
Context: Implementing shared decision-making is an international priority in chronic pain care, especially for primary care clinicians. Objective: We sought to understand decisional needs of people living with chronic pain in Canada. Study design and Analysis: Informed by the Checklist for Reporting Of Survey Studies, we conducted a population-based cross-sectional online survey across the 10 Canadian provinces. We reported national prevalence of decisional conflict. We identified difficult decisions in terms of diagnosis, treatment, daily living, and consultation and stratified results using respondents’ most difficult decision. We determined assumed and preferred role during the decision-making process as well as their congruence. Setting or Dataset: We gathered data from random samples registered within the Leger Marketing panel (i.e., a panel of 500,000 representative members of Canadian society with Internet access). Population studied: We recruited adults living with chronic noncancer pain across Canadian provinces. Outcome Measures: Based on the Ottawa Decision Support Framework, we collected data on difficult decisions faced when interacting with health care providers, the Decisional Conflict Scale (DCS≥25 or DCS≥37.5) and the Control Preferences Scale (assumed and preferred role). Results: We recruited 1,649 respondents with diverse socio-demographic profiles. Mean age was 51.8 years (SD=16.3). Half were man (51%), most lived in urban areas (88%), and pain duration ranged from 3 months to 59 years. We observed that 97% of respondents faced at least one difficult decision across their care pathways. We found thirty-six difficult decisions related to medical consultation, diagnosis, treatment, and daily life. Half (49%) of respondents made their most difficult decision with a primary care physician. Nationally, two third (69%) of respondents experienced decisional conflict (DCS score>25) and one third (34%) clinically significant decisional conflict (DCS>37.5). Two-thirds (68%) of respondents self-reported having a collaborative role during their decision while 76% wanted this role. Conclusions: People living with chronic pain have unmet decisional needs limiting optimal decisions to manage their chronic pain. Our findings will guide development of shared decision-making interventions to reduce decisional conflict at a national level, especially to support primary care where most discussions about difficult decisions occur.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".