Two for the price of one: The benefits of job sharing to increase women representation in surgical specialties
Bibliographic record
Abstract
Background: Women represent over 50% of medical school classes in Canada, yet only 36.8% of surgical residency applicants identified as female from 1995-2019. One potential explanation for this discrepancy is the lack of work-life balance. Job sharing is an alternative work schedule in which two employees share the responsibilities of one full-time job. Although job sharing is not common in medicine, it may provide a solution to this issue. This paper proposes the implementation of job sharing to increase women representation in surgical specialties and discusses the benefits it would provide to patients, physicians, and the healthcare system. Methods: The authors developed a pitch for job sharing in medicine after conducting a review of the literature as part of their participation in the Cutting Edge Womxn in Surgery Hackathon at Dalhousie University. Results: Job sharing has been successfully implemented in other industries and could have numerous benefits in medicine, such as preventing burnout and increasing women representation in surgical specialties. Physicians who practice job sharing report feeling supported while having improved work-life balance. Conclusion: Job sharing is a promising solution to increase women representation in surgical specialties and prevent burnout among physicians. The implementation of job sharing would benefit patients, physicians, and administration. By targeting excessive workload and promoting work-life balance, physicians can feel more satisfied in their roles and provide higher quality care to their patients. Job sharing warrants further exploration as a potential solution to the underrepresentation of women in surgical specialties and the burnout epidemic in the medical profession.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".