The development of a stakeholder-endorsed national strategic plan for advancing pain education across Canadian physiotherapy programs
Bibliographic record
Abstract
Background: The Canadian Pain Task Force recently advanced an action plan calling for improved entry-level health professional pain education. However, there is little research to inform the collaboration and coordination across stakeholders that is needed for its implementation. Aims: This article reports on the development of a stakeholder-generated strategic plan to improve pain education across all Canadian physiotherapy (PT) programs. Methods: = 2). Strategic priorities were developed through three steps: (1) stakeholder-generated data were collected and analyzed, (2) a draft strategic plan was developed and refined, and (3) stakeholder endorsement of the final plan was assessed. The project was primarily implemented online between 2016 and 2018. Results: The plan was developed through five iterative versions. Stakeholders unanimously endorsed a plan that included five priorities focusing on uptake of best evidence across (1) national PT governance groups and (2) within individual PT programs; (3) partnering with people living with pain in pain education; (4) advocacy for the PT role in pain management; and (5) advancing pain education research. Conclusion: This plan is expected to help Canadian stakeholders work toward national improvements in PT pain education and to serve as a useful template for informing collaboration on entry-level pain education within other professions and across different geographic regions.
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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 | 0.115 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".