Getting to uptake: do communities of practice support the implementation of evidence-based practice?
Bibliographic record
Abstract
INTRODUCTION: Practitioners are increasingly encouraged to adopt evidence-based practices (EBP) leading to a need for new knowledge translation strategies to support implementation and practice change. This study examined the benefits of a community of practice in the context of Ontario's children's mental health sector where organizations are mandated to adopt a standardized outcome measure to monitor client response to treatment. METHOD: Readiness for change, practice change, content knowledge, and satisfaction with and use of implementation supports were examined among practitioners newly trained on the measure who were randomly assigned to a community of practice (CoP) or a practice as usual (PaU) group. CoP practitioners attended 6 sessions over 12 months; PaU practitioners had access to usual implementation supports. RESULTS: Groups did not differ on readiness for change or reported practice change, although CoP participants demonstrated greater use of the tool in practice, better content knowledge and were more satisfied with implementation supports than PaU participants. CONCLUSION: CoPs present a promising model for translating EBP knowledge and promoting practice change in children's mental health that requires further study.
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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 | 0.021 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".