A realist evaluation of the role of communities of practice in changing healthcare practice
Bibliographic record
Abstract
BACKGROUND: Healthcare organisations seeking to manage knowledge and improve organisational performance are increasingly investing in communities of practice (CoPs). Such investments are being made in the absence of empirical evidence demonstrating the impact of CoPs in improving the delivery of healthcare. A realist evaluation is proposed to address this knowledge gap. Underpinned by the principle that outcomes are determined by the context in which an intervention is implemented, a realist evaluation is well suited to understand the role of CoPs in improving healthcare practice. By applying a realist approach, this study will explore the following questions: What outcomes do CoPs achieve in healthcare? Do these outcomes translate into improved practice in healthcare? What are the contexts and mechanisms by which CoPs improve healthcare? METHODS: The realist evaluation will be conducted by developing, testing, and refining theories on how, why, and when CoPs improve healthcare practice. When collecting data, context will be defined as the setting in which the CoP operates; mechanisms will be the factors and resources that the community offers to influence a change in behaviour or action; and outcomes will be defined as a change in behaviour or work practice that occurs as a result of accessing resources provided by the CoP. DISCUSSION: Realist evaluation is being used increasingly to study social interventions where context plays an important role in determining outcomes. This study further enhances the value of realist evaluations by incorporating a social network analysis component to quantify the structural context associated with CoPs. By identifying key mechanisms and contexts that optimise the effectiveness of CoPs, this study will contribute to creating a framework that will guide future establishment and evaluation of CoPs in healthcare.
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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 | 0.186 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".