Risk factors of clinically significant decisional conflict in people living with chronic pain
Bibliographic record
Abstract
Context: Decision-making in chronic pain care is characterized by a high level of decisional conflict (i.e., uncertainty about the course of action) leading to potentially reduced health outcomes. Most of difficult decisions on pain management are faced in primary care. Evidence outlines that shared decision-making could reduce decisional conflict, but current interventions in chronic pain had limited impact. Identifying factors of decisional conflict and target them with shared decision-making interventions is required to improve people-centred pain care. Objective: To identify risk factors of clinically significant decisional conflict from a national survey across Canada. Study design and Analysis: We conducted a population-based cross-sectional online survey in the 10 Canadian provinces. We used the recommendations of the Strengthening Analytical Thinking for Observational Studies to develop our statistical analysis plan. We used multilevel binary logistic regression models to identify risk factors, reported as odds ratios. Setting or Dataset: We gathered data from random samples registered within the Leger 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. Outcome Measures: The dependent variable was decisional conflict (measured with the Decisional Conflict Scale). Independent variables were decisional needs reported in the Ottawa Decision Support Framework. Results: In this national cross-sectional online survey of 1373 random respondents with diverse socio-demographic profiles, we found that moderate health literacy (OR=2.4 [1.6; 3.6]) and incomplete (OR=1.5 [1; 2.1]) or no (OR=2 [1.3; 3.2]) prior knowledge on the options are statistically significant modifiable risk factors that increase the risk of clinically significant decisional conflict. Perception of having assumed a collaborative role (OR=0.5 [0.3; 0.7]), congruence between preferred and assumed role (OR=0.6 [0.4; 0.8]), and decision self-efficacy (OR=0.65 [0.6; 0.7]) are modifiable risk factors that reduce the risk of clinically significant decisional conflict. Conclusions: The risk factors identified in this national study revealed that most of them are modifiable with comprehensive shared decision-making interventions. These modifiable risk factors should be considered by primary care clinicians when discussing pain management with a person living with chronic pain.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".