Representation of Women in Canadian Radiation Oncology Trainees and Radiation Oncologists: Progress or Regress?
Bibliographic record
Abstract
Purpose: The study objective was to determine the representation of women in Canadian radiation oncology (RO) trainees and the radiation oncologist workforce over time. Methods and Materials: Gender data for Canadian RO trainees (residents and fellows) and radiation oncologists were collected from the Canadian Post-MD Education Registry (1994-2021) and Canadian Medical Association (1994-2019). Visa trainees were excluded. Gender parity was defined as a 1:1 female-to-male ratio. Descriptive statistics were used to summarize the data. Results: = .011). Gender parity was observed in RO trainees between 2012 and 2016. The annual number of RO trainees ranged from 66 to 173 with 2 near-parallel periods of gender-associated growth (1994-1996; 2002-2008) and regression (1997-2001; 2009-2016) followed by gender divergence (2017-2021) with increasing male and decreasing female trainees. Nearly all Canadian regions, except Ontario, reached 50% or higher female representation in RO trainees during the study period. In the radiation oncologist workforce, female representation increased from 20% (54/271) to 37% (217/582) between 1994 and 2019, and all regions and age groups demonstrated higher female representation over time. Within radiation oncologist subgroups, age <35 years old and Quebec region cohorts reached gender parity. Conclusions: Representation of women varied in Canadian RO trainees and has fallen since 2014, whereas female representation generally increased in the radiation oncologist workforce over time. Gender parity was observed in RO trainees, radiation oncologists <35 years old, and radiation oncologists in Quebec. Recent declining female representation among RO trainees is worrisome, and further study is warranted to identify potential gender-based barriers in attracting women to the specialty.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".