Conceptual and practical challenges for implementing the communities of practice model on a national scale - a Canadian cancer control initiative
Bibliographic record
Abstract
BACKGROUND: Cancer program delivery, like the rest of health care in Canada, faces two ongoing challenges: to coordinate a pan-Canadian approach across complex provincial jurisdictions, and to facilitate the rapid translation of knowledge into clinical practice. Communities of practice, or CoPs, which have been described by Etienne Wenger as a collaborative learning platform, represent a promising solution to these challenges because they rely on bottom-up rather than top-down social structures for integrating knowledge and practice across regions and agencies. The communities of practice model has been realized in the corporate (e.g., Royal Dutch Shell, Xerox, IBM, etc) and development (e.g., World Bank) sectors, but its application to health care is relatively new. The Canadian Partnership Against Cancer (CPAC) is exploring the potential of Wenger's concept in the Canadian health care context. This paper provides an in-depth analysis of Wenger's concept with a focus on its applicability to the health care sector. DISCUSSION: Empirical studies and social science theory are used to examine the utility of Wenger's concept. Its value lies in emphasizing learning from peers and through practice in settings where innovation is valued. Yet the communities of practice concept lacks conceptual clarity because Wenger defines it so broadly and sidelines issues of decision making within CoPs. We consider the implications of his broad definition to establishing an informed nomenclature around this specific type of collaborative group. The CoP Project under CPAC and communities of practice in Canadian health care are discussed. SUMMARY: The use of communities of practice in Canadian health care has been shown in some instances to facilitate quality improvements, encourage buy in among participants, and generate high levels of satisfaction with clinical leadership and knowledge translation among participating physicians. Despite these individual success stories, more information is required on how group decisions are made and applied to the practice world in order to leverage the potential of Wenger's concept more fully, and advance the science of knowledge translation within an accountability framework.
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 | 0.112 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.036 | 0.034 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".