Parental leave, childcare policies, and workplace bias for hepatology professionals: A national survey
Bibliographic record
Abstract
BACKGROUND: The presence of workplace bias around child-rearing and inadequate parental leave may negatively impact childbearing decisions and sex equity in hepatology. This study aimed to understand the influence of parental leave and child-rearing on career advancement in hepatology. METHODS: A cross-sectional survey of physician members of the American Association for the Study of Liver Diseases (AASLD) was distributed through email listserv in January 2021. The 33-item survey included demographic questions, questions about bias, altering training, career plans, family planning, parental leave, and work accommodations. RESULTS: Among 199 US physician respondents, 65.3% were women, and 83.4% (n = 166) were attendings. Sex and racial differences were reported in several domains, including paid leave, perceptions of bias, and child-rearing. Most women (79.3%) took fewer than the recommended 12 paid weeks of parental leave for their first child (average paid leave 7.5 wk for women and 1.7 for men). A majority (75.2%) of women reported workplace discrimination, including 83.3% of Black and 62.5% of Hispanic women. Twenty percent of women were asked about their/their partners' pregnancy intentions or child-rearing plans during interviews for training. Women were more likely to alter career plans due to child-rearing (30.0% vs. 15.9%, p = 0.030). Women were also more likely to delay having children than men (69.5% vs.35.9%). CONCLUSIONS: Women reported sex and maternity bias in the workplace and during training interviews, which was more frequently experienced by Black and Hispanic women. As two-thirds of women had children during training, it is a particularly influential time to reevaluate programmatic support to address long-term gender disparities in career advancement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".