Évaluation du contexte et des besoins décisionnels pour intégrer les soins centrés sur la personne dans la gestion de la douleur chronique au Canada : l’enquête pancanadienne DÉCIDE-DOULEUR
Bibliographic record
Abstract
INTRODUCTION. The federal strategic plan for pain recommends integrating shared decision-making into chronic pain consultations. However, current shared decision-making interventions in this specific context have shown limited effectiveness. This may be explained by the low quality of their development. To design a high-quality intervention, it is essential to first assess the decision-making context and needs. OBJECTIVES. 1. Identify the decision-making context, including experienced difficult decisions, the most difficult decision, and the roles assumed and preferred during decision-making. We also measured decision conflict and regret associated with the most difficult decision. 2. Identify decisional needs by determining factors associated with conflict and regret. METHODS. We conducted a cross-sectional pan-Canadian online survey following the Checklist for Reporting of Survey Studies recommendations. Using Leger Marketing’s panel, we recruited a stratified proportional random sample of adults living with non-cancer chronic pain. Data collected included: (i) difficult decisions faced, (ii) levels of decision conflict (measured using the Decision Conflict Scale (DCS)) and decision regret (measured using the Decision Regret Scale (DRS)), and (iii) decisional needs. We performed descriptive and multilevel regression analyses. RESULTS. We recruited 1,649 participants. Obj 1. 96% of participants reported facing at least one difficult decision during their care pathway. Obj 2. One-third (33.7%) experienced clinically significant decisional conflict (DCS score ≥ 37.5/100). We identified 17 factors associated with clinically significant decisional conflict. Obj 3. Half of the participants (50%) experienced a level of decision regret deemed important (DRS score > 25/100). We identified 15 factors associated with decision regret. CONCLUSION. People living with chronic pain in Canada face unmet decisional needs and require support to optimize their decisions in pain management. This cross-sectional study provides an understanding of the context and needs of this population, establishing the foundation for developing a prototype decision aid.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 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".