Fostering evidence-based practice in community-based rehabilitation: Strategies for implementation
Bibliographic record
Abstract
Occupational therapists around the world are taking up the challenge to implement an evidence-based practice approach to the development of occupational therapy services. The emphasis in applying evidence-based practice within occupational therapy has been strongly biomedical in focus. In South Africa, many occupational therapists work in communities where their work is largely community-based rehabilitation. With no examples of how evidence-based practice can be applied in such settings, therapists have struggled with how it may be used to inform their practice. This paper explores the concepts of evidence-based practice and community-based rehabilitation, and illustrates how evidence-based practice can be applied within community-based rehabilitation. Examples are provided to show how evidence-based practice can realistically be applied in community-based rehabilitation programmes with the intention of empowering therapists to begin using evidence as a basis for their practice. It further explores how evidence-based practice can be used by occupational therapists to inform decision-making related to the development of community-based rehabilitation programmes and services.Key words: evidence-based practice, community-based rehabilitation, occupational therapy, practice-based evidence
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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 | 0.487 | 0.452 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.030 | 0.030 |
| Open science | 0.009 | 0.047 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".