“It looks good”: How a provincial radiation oncology program benefits from implementing peer review across cancer centers.
Bibliographic record
Abstract
238 Background: Peer review (PR) in radiation oncology (RO) has been endorsed as an indicator of treatment quality in North America and internationally. The direct benefits of PR include quality assurance (QA) on individual treatment plans. The indirect benefits for radiation oncology departments or programs (ROPs) have been postulated but not systematically evaluated. We used a rigorous and comprehensive qualitative approach to explore the indirect benefits of PR across a jurisdiction of cancer care, and to identify factors that facilitate PR, barriers to implementation, and strategies for expansion of PR across centers. Methods: Semi-structured qualitative interviews were held with all RO heads and Radiation therapy Managers (or delegate) in all 14 Radiation Oncology Programs (ROPs) in Ontario, Canada. The interview questions were developed using a Knowledge Translation Theoretical Domains Framework (TDF), guided by the results of a previous survey of Ontario cancer centers and by expert consensus. Interviews were audiotaped and abstracted for relevant themes by two independent analysts. Results: All interviewees endorsed numerous direct and indirect benefits of PR, and identified multiple facilitators and barriers to the implementation of PR. Thematic saturation was achieved. The structure-process-outcome model was used to categorize the results. Key findings included the identification of 34 independent benefits (structure n = 4, process n = 17, outcome n = 13), 40 key barriers (structure n = 9, process n = 26, outcome n = 5), and 22 facilitators (structure n = 4, process n = 15, outcome n = 3). Beyond QA, commonly endorsed benefits included enhanced knowledge sharing, efficiency, standardization, and education. Multiple potential strategies for the expansion of PR activities were revealed. Conclusions: The qualitative exploration of Ontario ROPs acknowledges that multiple barriers and facilitators to PR exist while clearly establishing the indirect benefit of PR on ROPs. Understanding reported barriers and facilitators and exploration of suggested strategies will inform continued implementation and expansion of PR activities, and seem generalizable to other jurisdictions.
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 | 0.047 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".