Communities of practice: A jurisdictional approach to improving the quality of care in radiation medicine in Ontario.
Bibliographic record
Abstract
122 Background: The Radiation Treatment Program (RTP) at Cancer Care Ontario (CCO) established several Communities of Practice (CoPs), with the goal of improving radiation treatment (RT) quality and safety. The RTP identifies variation in practice and quality improvement (QI) opportunities in the 14 Regional Cancer Centres (RCCs) and facilitates the development of CoPs to share best practices and standardize care. Methods: Since 2010, the RTP has formed 7 CoPs ( > 185 members in total): 4 intra-disciplinary (Radiation Therapy, Medical Physics, Advanced Practice Radiation Therapy, Radiation Safety) and 3 inter-disciplinary (Head and Neck (HN), Gynecological (GYNE) and Lung Cancer). Members are recruited with the aim of securing engagement from all RCCs to ensure representation of regional diversity and to facilitate adoption of best practices. CoPs are supported with nominal funding and resources provided by CCO, but are led and driven by members, who identify and prioritize key quality issues and select corresponding QI projects to pursue. The RTP performs regular evaluation activities to assess initiative engagement and impact. Results: RTP CoPs have enhanced the quality and safety of RT delivery in Ontario through QI initiatives, advice documents and tools that have enabled: Improved RT safety (use of safety straps in RT delivery); Adoption of best practices (RT plan evaluation guidance); Education and knowledge transfer – (stereotactic body RT implementation and training framework); and Support for infrastructure improvements (recommendation for additional Magnetic Resonance-guided brachytherapy units) ( https://www.cancercare.on.ca/ocs/clinicalprogs/radiationtreatment/ ). Advice documents have improved alignment with recommended practice (40% and 50% absolute increases in two HN initiatives). Evaluation surveys indicate that members believe the CoPs have enhanced inter-regional communication and collaboration (89%), knowledge transfer/exchange (91%), and professional networking between RCCs (92%). Conclusions: CoPs can be a highly effective model for improving quality of care. The establishment of CoPs should be considered for QI in other areas of the healthcare system.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".