A knowledge translation implementation strategy to promote evidence-based practices in the management of lateral ankle sprains by Canadian Armed Forces physiotherapists
Bibliographic record
Abstract
Introduction: Lateral ankle sprains (LAS) are the third most common musculoskeletal injury among military members, resulting in considerable time loss and a substantial rehabilitation workload. The operational relevance of these consequences to the Canadian Armed Forces (CAF) should be minimized using evidence-based practices. The purpose of this research project was to use a comprehensive knowledge translation (KT) implementation strategy tailored for CAF physiotherapists to improve their knowledge and use of the rehabilitation interventions and outcome measures recommended in the management of LAS. Methods: All CAF physiotherapists were sent an email invitation to complete an online questionnaire investigating their knowledge and use of rehabilitation interventions and outcome measures recommended in the management of LAS. We then used a distance learning platform to facilitate a 3-hour active, multi-component KT intervention that included summarized research, practice tools, and guided interaction. The primary outcome was the median change reported on the online questionnaire prior to, and 3 months following the intervention. Results: Response rate to the online questionnaire was 75% ( n = 67/89). Respondents reported a change from very good to excellent knowledge and use of rehabilitation interventions recommended in LAS management between baseline and 3 months. Respondents reported a change from poor to good/excellent knowledge and use of outcome measures recommended in LAS management between baseline and 3 months. Respondents reported a KT intervention preference for summarized research. Discussion: Our findings suggest that a KT implementation strategy tailored for CAF physiotherapists can improve their self-reported knowledge and use of outcome measures recommended in LAS management.
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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 | 0.055 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".