Understanding Equity, Diversity, and Inclusion Within Canadian Radiation Oncology Training Programs: Experiences 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-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: 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.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".