Do Women Have Equal Chances for an Academic Career in Radiation Oncology in Canada? A Comparison With Related Specialties
Bibliographic record
Abstract
PurposeThe progress of women in academic medicine appears to be curtailed. We evaluated gender differences in academia for residents in radiation oncology compared with 2 of its related specialties, radiology and medical oncology, across Canada.Methods and MaterialsWe analyzed abstracts presented between 2013 and 2016 at the annual meetings of the Canadian Association of Radiation Oncologists and compared it to the corresponding data for the meetings of the Canadian Association of Radiologists and Canadian Association of Medical Oncology. We further evaluated gender composition of abstracts, presentations, and publications available on PubMed. Conversion rates according to gender and to medical specialties were assessed. Proportions were compared using Fisher exact test or the chi-squared test.ResultsAmong the 198 presented abstracts, 103 (52%) were published. Radiation oncology had the highest publishing rate with 90% (oncology 56%, radiology 40%). The publication rate between the medical specialties was significantly different (P < .001).Fifty-seven percent of abstracts presented by women were published versus 48% of abstracts presented by men. Overall, there was no significant difference between genders in terms of subsequent conversions into a scientific publication within each specialty (P = .25-1.0).In radiation oncology, women presented 67% of abstracts and published 95% of their presented abstracts, and in medical oncology, 66% of abstracts were from women and 57% of the presented abstracts were published. Among the published abstracts, 83% had the same first author in the abstract and the publication. Among those who lost their first-authorship status, 59% were women. However, there was no statistically significant difference between specialties for loss of first-author status.ConclusionsWe observed that from 2013 to 2016, women had the highest presentation and publication rate in radiation oncology. More prospective data are needed to monitor the progress of women in all specialties and their specific needs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".