Development of Communities of Practice to Facilitate Quality Improvement Initiatives in Surgical Oncology
Bibliographic record
Abstract
BACKGROUND: The process of developing clinical guidelines and standards for cancer treatment and screening is well established in the Ontario health care system; however, the dissemination and implementation of such guidelines and standards are more recent undertakings. Traditional implementation strategies to improve surgical practice and the delivery of cancer care have not been consistently effective. There is a recognized need to develop integrated models that offer direct support for implementation strategies. Such a model should be feasible, adaptable, and open to evaluation across diverse surgical settings. DISCUSSION: Research suggests that successful implementation should consider tools and expertise from other disciplines. This article considers a community of practice (COP) model to provide a supportive infrastructure for quality improvements in cancer surgery. The COP model was adapted for cancer surgeons. It is supported by 5 enablers referred to as tools: communication system, project development support, access to data, access to evidence review, and accreditation with continued medical education and continued professional development. These tools need to be part of an infrastructure that is both provided and supported by a team of administrators and health care professionals, who have active roles and responsibilities. Therefore, the primary objective of this article is to describe our COP model in cancer surgery including the key success factors necessary for providing the infrastructure and tools. The secondary objective is to offer the integrated COP model as a basis for future research and the evaluation of various collaborative improvement projects. SUMMARY: Building on knowledge management concepts, we identified the 4 essential processes that should be targeted by implementation strategies. A common COP evaluation framework uses the outcomes of 4 knowledge conversion modes-organizational memory, social capital, innovation, and knowledge transfer-as proxies for actual provider and organizational behavior. Insights from different collaborative improvement projects described in a consistent way could inform future research and assist in the collation of systematic reviews on this topic.
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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 | 0.181 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".