Communities of practice to improve public health outcomes: a systematic review
Bibliographic record
Abstract
Purpose Communities of practice (CoPs) exist to enable people to share knowledge, innovate and progress a common field of practice. This paper aims to identify whether CoPs have a measured impact on public health practice and the tools used to measure the impact and potential barriers and facilitators that may have been identified during the implementation of these CoPs. Design/methodology/approach A systematic review of the literature was conducted using PRISMA guidelines. Searches of six databases, Google Scholar and a citation search were completed. Included studies were from 1986 to 2016, involved the public health workforce and an evaluation of a CoP -like intervention. A narrative synthesis of the findings was conducted. Findings From 3,021 publications, 12 studies met inclusion criteria and described the impact of ten CoPs amongst public health practitioners from America, Canada, Australasia and the United Kingdom. CoPs support the prevention workforce to change their practice when they provide structured problem-solving, reflective practice and networking opportunities. None of the studies described the impact of CoPs on public health outcomes. Practical implications CoPs that provide structured problem-solving, reflective practice and diverse networking may effectively support the public health workforce. Existing methods used to evaluate CoPs lack rigour; thus, the true impact of CoPs on population health remains unknown. Originality/value This is the first known systematic review that has measured the impact of CoPs on the preventative health workforce and the conditions in which they have an impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".