Translating knowledge to practice: An occupational therapy perspective
Bibliographic record
Abstract
BACKGROUND/AIM: Translating knowledge to practice, also called 'knowledge translation', is increasingly recognised as a driving force to strengthen and improve the healthcare system. How knowledge translation fits with occupational therapy practice deserves examination. METHODS: This paper will explore how an action process model, the Knowledge-To-Action Process, may advance knowledge translation in occupational therapy. Occupational therapists typically view knowledge in a broad sense, encompassing research, tacit knowledge, expert opinion and client evidence. The Knowledge-To-Action Process facilitates application of client, therapist and research knowledge to occupational therapy practice. RESULTS: Examination of knowledge translation through the lens of the Knowledge-To-Action Process creates awareness of the value of client, therapist and research knowledge. It also highlights opportunities as practitioners to implement knowledge translation. CONCLUSIONS: Models able to flexibly reflect an occupational therapy perspective of knowledge have a potentially vital role in successful knowledge translation. Furthermore, these models allow therapists and other stakeholders to analyse complex situations and identify targeted knowledge translation strategies.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.060 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".