Oncology Communities of Practice: Insights from a Qualitative Analysis
Bibliographic record
Abstract
Background: A community of practice (cop) is formally defined as a group of people who share a concern or a passion for something they do and who learn how to do it better as they interact regularly. Communities of practice represent a promising approach for improving cancer care outcomes. However, little research is available to guide the development of oncology cops. In 2015, our urban community hospital launched an oncology cop, with the goals of decreasing barriers to access, fostering collaboration, and improving practitioner knowledge of guidelines and services in cancer care. Here, we share insights from a qualitative analysis of feedback from participants in our cop. The objective of the project was to identify participant perspectives about preferred cop features, with a view to improving the quality of our community hospital's oncology cop. Methods: After 5 in-person meetings of our oncology cop, participants were surveyed about what the cop should start, stop, and continue doing. Qualitative methods were used to analyze the feedback. Results: The survey collected 250 comments from 117 unique cop participants, including family physicians, specialist physicians, nurses, and allied health care practitioners. Analysis identified participant perspectives about the key features of the cop and avenues for improvement across four themes: supporting knowledge exchange, identifying and addressing practice gaps, enhancing interprofessional collaboration, and fostering a culture of partnership. Conclusions: Based on the results, we identified several considerations that could be helpful in improving our cop. Our findings might help guide the development of oncology cops at other institutions.
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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 | 0.040 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".