Building an Oncology Community of Practice to Improve Cancer Care
Bibliographic record
Abstract
Background: Communities of practice (cops) have been shown to be effective models for achieving quality outcomes in health care. Objective: Here, we describe the application of the cop model to the Canadian oncology context. Methods: We established an oncology cop at our urban community hospital and its networks. Goals were to decrease barriers to access, foster collaboration, and improve knowledge of guidelines in cancer care. We hosted 6 in-person multidisciplinary meetings, focusing on screening, diagnosis, and management of common solid tumours. Health care providers affiliated with our hospital were invited to attend and to complete post-meeting surveys. Likert scales assessed whether cop goals were realized. Results: Meetings attracted a mean of 57 attendees (range: 48-65 attendees), with a mean of 84% completing the surveys and consenting to the analysis. Attendees included family physicians (mean: 41%), specialist physicians (mean: 24%), nurses (mean: 10%), and allied health care providers (mean: 22%). Repeat attendance increased during the series, with 85% of attendees at the final meeting having attended 1 or more prior meetings. Across the series, most participants agreed or strongly agreed that the cop reduced barriers (mean: 76.0% ± 7.9%) and improved access to cancer care services (mean: 82.4% ± 8.1%) and subject matter experts (mean: 91.7% ± 4.2%); fostered teamwork (mean: 84.5% ± 6.8%) and a culture of collaboration (mean: 94.8% ± 4.2%); improved knowledge of cancer care services (mean: 93.3% ± 4.8%), standards of practice (mean: 92.3% ± 3.1%), and quality indicators (mean: 77.5% ± 6.3%); and improved cancer-related practice (mean: 88.8% ± 4.6%) and satisfaction in caring for cancer patients (mean: 82.9% ± 6.8%). Participant feedback carried a potential for bias. Conclusions: We demonstrated the feasibility of oncology cops and found that participants perceived their value in reducing barriers to access, fostering collaboration, and improving knowledge of guidelines in cancer care.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".