Stresses on women physicians: Consequences and coping techniques
Bibliographic record
Abstract
We review current data on types of stressors acting on women physicians, the consequences of these stressors and methods of coping with them. We undertook a systematic review of original articles published in the last 15 years and registered mainly on Medline and on the internet websites focusing on these issues. In addition to the pressures acting on all physicians, women physicians face specific stressors related to discrimination, lack of role models and support, role strain, and overload. The depression rate in women physicians does not vary from that of the general public but the rates of successful suicide and divorce are much higher. Women in academic settings are promoted more slowly, have lower salaries, receive fewer resources, and suffer from a range of micro-inequities. They often lack mentors to provide advice and guidance. They must cope with the pressures of choosing when to have a child and conflicts between being a wife and mother and having a career. Despite these pressures, they report a high degree of career satisfaction. Although women physicians suffer from a variety of stressors that can lead to career impediments, stress reactions, and psychiatric problems, generally they are satisfied with their careers. Personal coping techniques can help women deal with these stressors. Pressures will continue until attitudes and practices change in institutional settings. Some institutions are initiating changes to end discrimination against women faculty.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".