Use of communities of practice in business and health care sectors: A systematic review
Bibliographic record
Abstract
BACKGROUND: Since being identified as a concept for understanding knowledge sharing, management, and creation, communities of practice (CoPs) have become increasingly popular within the health sector. The CoP concept has been used in the business sector for over 20 years, but the use of CoPs in the health sector has been limited in comparison. OBJECTIVES: First, we examined how CoPs were defined and used in these two sectors. Second, we evaluated the evidence of effectiveness on the health sector CoPs for improving the uptake of best practices and mentoring new practitioners. METHODS: We conducted a search of electronic databases in the business, health, and education sectors, and a hand search of key journals for primary studies on CoP groups. Our research synthesis for the first objective focused on three areas: the authors' interpretations of the CoP concept, the key characteristics of CoP groups, and the common elements of CoP groups. To examine the evidence on the effectiveness of CoPs in the health sector, we identified articles that evaluated CoPs for improving health professional performance, health care organizational performance, professional mentoring, and/or patient outcome; and used experimental, quasi-experimental, or observational designs. RESULTS: The structure of CoP groups varied greatly, ranging from voluntary informal networks to work-supported formal education sessions, and from apprentice training to multidisciplinary, multi-site project teams. Four characteristics were identified from CoP groups: social interaction among members, knowledge sharing, knowledge creation, and identity building; however, these were not consistently present in all CoPs. There was also a lack of clarity in the responsibilities of CoP facilitators and how power dynamics should be handled within a CoP group. We did not find any paper in the health sector that met the eligibility criteria for the quantitative analysis, and so the effectiveness of CoP in this sector remained unclear. CONCLUSION: There is no dominant trend in how the CoP concept is operationalized in the business and health sectors; hence, it is challenging to define the parameters of CoP groups. This may be one of the reasons for the lack of studies on the effectiveness of CoPs in the health sector. In order to improve the usefulness of the CoP concept in the development of groups and teams, further research will be needed to clarify the extent to which the four characteristics of CoPs are present in the mature and emergent groups, the expectations of facilitators and other participants, and the power relationship within CoPs.
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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 | 0.025 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".