Understanding the Role of Communities of Practice in Evidence-Informed Decision Making in Public Health
Bibliographic record
Abstract
In this article we report on qualitative findings that describe public health practitioners' practice-based definitions of evidence-informed decision making (EIDM) and communities of practice (CoP), and how CoP could be a mechanism to enhance their capacity to practice EIDM. Our findings emerged from a qualitative descriptive analysis of group discussions and participant concept maps from two consensus-building workshops that were conducted with public health practitioners (N = 90) in two provinces in eastern Canada. Participants recognized the importance of EIDM and the significance of integrating explicit and tacit evidence in the EIDM process, which was enhanced by CoP. Tacit knowledge, particularly from peers and personal experience, was the preferred source of knowledge, with informal peer interactions being the favored form of CoP to support EIDM. CoP helped practitioners build relationships and community capacity, share and create knowledge, and build professional confidence and critical inquiry. Participants described individual and organizational attributes that were needed to enable CoP and EIDM.
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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 | 0.137 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.015 | 0.069 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".