Impact of department chair gender on paid parental leave across American anaesthesiology residencies
Bibliographic record
Abstract
BACKGROUND: Residency training and parenthood are conflicting pursuits for many residents, as both often occur during similar years of life. Online policy about paid parental leave for residents is important for not only mitigating this dilemma, but also ensuring that the associated health benefits can be fully capitalised on. PURPOSE: Investigate the extent of advertised paid parental leave for anaesthesiology residencies in the USA and to explore whether this had an association with the gender of the department chair for these programmes. Analysis of Canadian anaesthesiology residencies was performed to assess whether a nation with federally protected paid parental leave yielded disparate rates of advertised paid parental leave. METHODS: analysis was used to determine if there was a statistically significant association between the gender of anaesthesiology residency department chairs and paid parental leave advertised. Rates of advertised paid parental leave were compared with those seen in Canadian anaesthesiology residencies. RESULTS: US anaesthesiology residency department chairs were 84% (137/164) men. Of the men-led programmes, 42% (58/137) advertised paid parental leave, while 70% (19/27) of women-led programmes advertised such benefits (p<0.05). Overall, 47% of (77/164) of US anaesthesiology residencies advertised paid parental leave, while 76% (13/17) of Canadian anaesthesiology residencies advertised paid parental leave (p<0.05). CONCLUSION: In the USA, anaesthesiology residencies with department chairs held by women had a higher rate of advertised paid parental leave. Such findings call attention to the potential downstream effects of lacking diversity in leadership within medicine. When compared with the USA, Canada was found to have higher rates of advertised paid parental leave across their anaesthesiology residencies, potentially highlighting the impact of federal legislature on medical residents.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".