Best Practice Recommendations for the Development, Implementation, and Evaluation of Online Knowledge Translation Resources in Rehabilitation
Bibliographic record
Abstract
The knowledge-to-practice gap in rehabilitation has spurred knowledge translation (KT) initiatives aimed at promoting clinician behavior change and improving patient care. Online KT resources for physical therapists and other rehabilitation clinicians are appealing because of their potential to reach large numbers of individuals through self-paced, self-directed learning. This article proposes best practice recommendations for developing online KT resources that are designed to translate evidence into practice. Four recommendations are proposed with specific steps in the development, implementation, and evaluation process: (1) develop evidence-based, user-centered content; (2) tailor content to online format; (3) evaluate impact; and (4) share results and disseminate knowledge. Based on KT evidence and instructional design principles, concrete examples are provided along with insights gained from experiences in creating and evaluating online KT resources for physical therapists. In proposing these recommendations, the next steps for research are suggested, and others are invited to contribute to the discussion.
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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 | 0.277 | 0.440 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".