Understanding Equity, Diversity, and Inclusion Within Canadian Radiation Oncology Training Programs: A National Survey of Residents and Fellows
Bibliographic record
Abstract
Background: This study characterizes the current representation of sociodemographic groups within Canadian radiation oncology training programs and trainees’ lived experiences. Methods: A 59-item ethics-approved, bilingual survey assessed sociodemographics, training perceptions, mentorship, discrimination/harassment experienced, and open-ended questions. Electronic surveys were distributed to all Canadian radiation oncology residents/fellows. Descriptive statistics summarized survey responses. Categorical groups were compared using chi-squared/Fisher’s exact tests. Thematic analysis was performed on open-ended responses. Results: Between July and December 2023, 98 of 177 (56%) trainees participated: 70% were residents, 52% identified as male, 62% as a racialized minority, and 10% as a sexual minority. Most respondents reported training program satisfaction (83%) and a respectful workplace culture (69%); however, discrimination during training was reported by 38%. Less than half (45%) felt comfortable reporting discrimination/harassment within their workplace. Women were more likely to feel under-represented in-training (46% vs. 13%, p = 0.001) and perceived more discrimination events (64% vs. 19%, p < 0.001). Three themes emerged as follows: importance of offering EDI education, ensuring pathways for reporting learner mistreatment, and creating appropriately diverse selection committees. Conclusions: Although most Canadian radiation oncology trainees reported satisfaction and a respectful culture, key differences between groups were observed. Targeted strategies and stronger institutional policies to improve representation and reduce rates of discrimination/harassment are needed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".