Exploring the evidence base for Communities of Practice in health research and translation: a scoping review
Bibliographic record
Abstract
Abstract Background The translation of research into healthcare practice relies on effective communication between disciplines, however strategies to address the gap between information sharing and knowledge transfer are still under exploration. Communities of Practice (CoP) are informal networks of stakeholders with shared knowledge or endeavour and present an opportunity to address this gap beyond disciplinary boundaries. However, the evidence-base supporting their development, implementation and efficacy in health is not well described. This review explores the evidence underpinning the use of CoP in health research and translation. Methods A scoping review was undertaken using Arksey and O'Malley's methodological framework. A comprehensive search of health databases and grey literature was performed using keywords and controlled vocabulary. Studies were not restricted by date or research method. Results A total of 1355 potentially relevant articles were identified through the global search strategy. Following screening, six articles were retained for analysis. Included studies were published between 2002 and 2013 in the United Kingdom (n = 3), Canada (n = 2) and Italy (n = 1). Three papers reported primary research; one used a quantitative methodology, one a qualitative, and one a descriptive evaluation approach. The three remaining papers explored seminal and evolving theories of CoP in the context of knowledge transfer and translation to the health sector. Conclusions A paucity of evidence exists regarding the development and efficacy of CoP in health research and translation. Further empirical research is required to determine if communities of practice can enhance the translation of research into clinical practice.
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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 | 0.196 | 0.520 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.072 | 0.057 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".