Decisional needs assessment for patient-centred pain care in Canada: the DECIDE-PAIN study protocol
Bibliographic record
Abstract
Introduction The 2021 Action Plan for Pain from the Canadian Pain Task Force advocates for patient-centred pain care at all levels of healthcare across provinces. Shared decision-making is the crux of patient-centred care. Implementing the action plan will require innovative shared decision-making interventions, specifically following the disruption of chronic pain care during the COVID-19 pandemic. The first step in this endeavour is to assess current decisional needs (ie, decisions most important to them) of Canadians with chronic pain across their care pathways. Methods and analysis Design Grounded in patient-oriented research approaches, we will perform an online population-based survey across the ten Canadian provinces. We will report methods and data following the CROSS reporting guidelines. Sampling The Léger Marketing company will administer the online population-based survey to its representative panel of 500 000 Canadians to recruit 1646 adults (age ≥18 years old) with chronic pain according to the definition by the International Association for the Study of Pain (eg, pain ≥12 weeks). Content Based on the Ottawa Decision Support Framework, the self-administered survey has been codesigned with patients and contain six core domains: (1) healthcare services, consultation and postpandemic needs, (2) difficult decisions experienced, (3) decisional conflict, (4) decisional regret, (5) decisional needs and (6) sociodemographic characteristics. We will use several strategies such as random sampling to improve survey quality. Analysis We will perform descriptive statistical analysis. We will identify factors associated with clinically significant decisional conflict and decision regret using multivariate analyses. Ethics and dissemination Ethics was approved by the Research Ethics Board at the Research Centre of the Centre Hospitalier Universitaire de Sherbrooke (project #2022-4645). We will codesign knowledge mobilisation products with research patient partners (eg, graphical summaries and videos). Results will be disseminated via peer-reviewed journals and national and international conferences to inform the development of innovative shared decision-making interventions for Canadians 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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.005 |
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".