Diversity and Experiences of Radiation Oncologists in Canada: A Survey of Gender Identity, Sexual Orientation, Disability, Race, Ethnicity, Religion and Workplace Discrimination
Bibliographic record
Abstract
Background: This study’s objective was to be the first to explore the ethnicity/cultural origins, gender identity, ability/disability, sexual orientation, socioeconomic background and harassment/discrimination experiences of Canadian radiation oncologists (ROs). Methods: Following a literature review and input from content experts, an eth-ics-approved survey was developed in English and French and electronically distributed to all ROs in Canada (n=598). Descriptive statistics summarized responses. Comparisons between groups were performed using Chi-square tests and content analysis was per-formed on open-ended responses. Results: The survey response rate was 48% (298/598). Most respondents were male (62%), 35-44 years old (39%) and heterosexual (90%). 41% identified as belonging to a racialized group, which is higher than the overall Canadian population (27%), but Black, Indigenous and South Asian ROs were underrepresented (2% vs 4%, < 1% vs 5% and 1% compared to 4% respectively). A significant subset analysis showed that only 20% (21/105) of racialized ROs were women whereas Caucasian women comprised 49% (74/150) of Caucasian respondents (p < 0.001). While 75% of re-spondents reported job satisfaction, 42% reported experiencing workplace discrimina-tion/harassment within the past 5 years; most commonly this was perpetrated by fellow faculty (32%; 58/183) or patients or their family members (32%; 58/183). Respondents felt that gender, race/ethnicity, and age were the three top reasons for discrimina-tion/harassment with double the amount of racialized ROs reporting harassment com-pared to White ROs (p < 0.001). Nearly half (45%; 114/252) did not understand how to report, or felt uncomfortable reporting, workplace discrimination/harassment. Conclu-sions: This study highlights high harassment and discrimination rates amongst Canadian ROs, especially amongst racialized women, which may affect career satisfaction and attrition rates. Compared to census data, Black, Indigenous and South Asian ROs were underrepresented, and amongst racialized ROs, racialized women were significantly underrepresented. These findings underscore the need for targeted diversity initiatives, improved mentorship programs, and stronger institutional policies to address harass-ment and foster an inclusive work environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".