Communities of practice for supporting health systems change: a missed opportunity
Bibliographic record
Abstract
BACKGROUND: Communities of practice (CoPs) have been used in the health sector to support professional practice change. However, little is known about how CoPs might be used to influence a system that requires change at and across various levels (i.e. front line care, organizational, governmental). In this paper we examine the experience of a CoP in the Canadian province of Ontario as it engages in improving the care of seniors. Our aim is to shed light on using CoPs to facilitate systems change. METHODS: This paper draws on year one findings of a larger multiple case study that is aiming to increase understanding of knowledge translation processes mobilized through CoPs. In this paper we strategically report on one case to illustrate a critical example of a CoP trying to effect systems change. Primary data included semi-structured interviews with CoP members (n = 8), field notes from five planning meetings, and relevant background documents. Data analysis included deductive coding (i.e. pre-determined codes aligned with the larger project) and inductive coding which allowed codes and themes to emerge. A thorough description of the case was prepared using all the coded data. RESULTS: The CoP recognized a need to support health professionals (nurses, dentists) and related paraprofessionals with knowledge, experience, and resources to appropriately address their clients' oral health care needs. Accordingly, the CoP led a knowledge-to-action initiative that involved a seven-part webinar series meant to transfer step-by-step, skill-based knowledge through live and archived webinars. Although the core planning team functioned effectively to develop the webinars, the CoP was challenged by organizational and long-term care sector cultures, as well as governmental structures within the broader health context. CONCLUSION: The provincial CoP functioned as an incubator that brought together best practices, research, experiences, a reflective learning cycle, and passionate champions. Nevertheless, the CoP's efforts to stimulate practice changes were met with broader resistance. Research about how to use CoPs to influence health systems change is needed given that CoPs are being tasked with this goal.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".