A strategic initiative to facilitate knowledge translation research in rehabilitation
Bibliographic record
Abstract
BACKGROUND: While there is a growing body of literature supporting clinical decision-making for rehabilitation professionals, suboptimal use of evidence-based practices in that field persists. A strategic initiative that ensures the relevance of the research and its implementation in the context of rehabilitation could 1) help improve the coordination of knowledge translation (KT) research and 2) enhance the delivery of evidence-based rehabilitation services offered to patients with physical disabilities. This paper describes the process and methods used to develop a KT strategic initiative aimed at building capacity and coordinating KT research in physical rehabilitation and its strategic plan; it also reports the initial applications of the strategic plan implementation. METHODS: We used a 3-phase process consisting of an online environmental scan to identify the extent of KT research activities in physical rehabilitation in Quebec, Canada. Data from the environmental scan was used to develop a strategic plan that structures KT research in physical rehabilitation. Seven external KT experts in health science reviewed the strategic plan for consistency and applicability. RESULTS: Sixty-four KT researchers were identified and classified according to the extent of their level of involvement in KT. Ninety-six research projects meeting eligibility criteria were funded by eight of the fourteen agencies and organizations searched. To address the identified gaps, a 5-year strategic plan was developed, containing a mission, a vision, four main goals, nine strategies and forty-two actions. CONCLUSION: Such initiatives can help guide researchers and relevant key stakeholders, to structure, organize and advance KT research in the field of rehabilitation. The strategies are being implemented progressively to meet the strategic initiative's mission and ultimately enhance users' rehabilitation services.
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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 | 0.251 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".