Barriers to Physiotherapists’ Use of Professional Development Tools for Chronic Pain: A Knowledge Translation Study
Bibliographic record
Abstract
Purpose: The Pain Science Division (PSD) is a special interest group of the Canadian Physiotherapy Association that serves physiotherapists who have an interest in better understanding and managing patients’ pain. The PSD developed evidence-based resources for its members with the goal of improving patient care by supporting professional development. However, online metrics tracking access to these resources indicated that access was low. The purpose of this study was to identify the barriers PSD members encountered to the use of PSD resources and to recommend interventions to address these barriers guided by the Theory and Techniques Tool (TTT). Method: We distributed an online survey to PSD members across Canada. We used the TTT, a knowledge translation tool, to guide the design of the questionnaire and identify actionable findings. Results: Response rates from 621 non-student members and 1,470 student members were 26.9% and 1.4%, respectively. Based on the frequency of practising physiotherapists’ ( n = 167) agreement with items in the TTT, the primary barriers to use of the PSD resources were forgetting that the resources were available and forgetting to use them. Conclusions: The TTT can be used to identify barriers to use of professional development tools.
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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 | 0.034 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".