Communities of practice in Alberta Health Services: advancing a learning organisation
Bibliographic record
Abstract
BACKGROUND: In 2009, Alberta Health Services (AHS) became Canada's first and largest fully integrated healthcare system, involving the amalgamation of nine regional health authorities and three provincial services. Within AHS, communities of practice (CoPs) meet regularly to learn from one another and to find ways to improve service quality. This qualitative study examined CoPs as an applied practice of a learning organisation along with their potential influence in a healthcare system by exploring the perspectives of CoP participants. METHODS: A collective case study method was used to enable the examination of a cross-section of cases in the study organisation. Semi-structured interviews were conducted with 31 participants representing 28 distinct CoPs. Using Senge's framework of a learning organisation, CoP influences associated with team learning and organisational change were explored. RESULTS: CoPs in AHS were described as diverse in practice domains, focus, membership boundaries, attendance and sphere of influence. Using small-scale resource investments, CoPs provided members with opportunities for meaningful interactions, the capacity to build information pathways, and enhanced abilities to address needs at the point of care and service delivery. Overall, CoPs delivered a sophisticated array of engagement and knowledge-sharing activities perceived as supportive of organisational change, systems thinking, and the team learning practice critical to a learning organisation. CONCLUSION: CoPs enable the diverse wealth of knowledge embedded in people, local conditions and special circumstances to flow from practice domain groups to programme and service areas, and into the larger system where it can effect organisational change. This research highlights the potential of CoPs to influence practice and broad-scale change more directly than previously understood or reported in the literature. As such, this study suggests that CoPs have the potential to influence and advance widespread systems change in Canadian 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 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".