People living with chronic pain in Canada face difficult decisions and decisional conflict concerning their care: data from the national DECIDE-PAIN survey
Bibliographic record
Abstract
Shared decision-making is an imperative in chronic pain care. However, we know little about the decision-making process, especially in primary care where most chronic pain care is provided. We sought to understand decisional needs of people living with chronic pain in Canada. We conducted a population-based cross-sectional online survey of random samples of adults living in Canada with chronic noncancer pain and registered with the Leger Marketing panel. We followed the International Association for Study of Pain definition of chronic pain (i.e., persistent or recurrent pain lasting longer than three months). We used a stratified proportional random sampling based on the population and chronic pain prevalence of each province to achieve representativeness. Based on the Ottawa Decision Support Framework, we collected data on difficult decisions (i.e., decision with more than one option and no clear best option) related to their chronic pain condition, the level of decisional conflict associated with the most difficult decisions (i.e., Decisional Conflict Scale), the assumed and preferred role during the decision-making process (i.e., Control Preferences Scale), and respondents’ characteristics. We used descriptive quantitative analyses of survey responses. Of the 31,545 invited panellists, 2,666 met the eligibility criteria, and 1,649 respondents from the 10 Canadian provinces completed the survey. Respondents had diverse socio-demographic profiles. Mean age was 51.8 years (SD = 16.3). Half were men (51.4%), most lived in urban areas (87.8%), mean pain duration was 8.5 years (SD = 9.6), and respondents reported an average number of painful body regions of 2.3 (SD = 1.5). We observed that 96.7% of respondents faced at least one difficult decision across their care pathways. These difficult decisions were related to numerous issues from the medical consultation, diagnosis, treatment, and daily life. Almost half of respondents made their most difficult decision with a primary care physician. One third of respondents experienced a high level of clinically significant decisional conflict (Decisional Conflict Scale score ≥ 37.5). Two-thirds of respondents self-reported having a collaborative role during their decision while three-quarters wanted this role. People living with chronic pain in Canada have unmet decisional needs and need support to make optimal decisions to manage their chronic pain. Our findings will guide future development of interventions to implement shared decision-making, especially to support primary care where discussions about difficult decisions often occur. 1. We conducted an online survey across 10 Canadian provinces and collected responses from a wide diversity of people living with chronic pain. 2. Over 96% of respondents faced at least one difficult decision concerning pain care across their care pathways, from diagnosis to daily living modifications. 3. A third of respondents experienced a high level of clinically significant decisional conflict. 4. People living with chronic pain in Canada have unmet needs and desire to be involved in the decisions about their pain care. 5. Our national survey justifies the development of shared decision-making interventions, especially to support primary care, to address difficult decisions, reduce decisional conflict and increase involvement in chronic pain care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".