Gender Differences in Domestic Responsibilities of Otolaryngologists—A Mixed‐Methods Analysis
Bibliographic record
Abstract
OBJECTIVES: Female otolaryngologist-head and neck surgeons (OHNS) confront unique barriers. This study examines the influence of home life, especially gendered division of household labor, on leadership, productivity, and burnout. METHODS: A survey was distributed through social media and national society list-serv. Demographics, responsibility for household roles, and Maslach Burnout Inventory for Medical Personnel were included. Participants were invited to participate in semi-structured interviews, employing purposive sampling, with qualitative thematic analysis. RESULTS: Response rate was 26.4% (145 of 550 of eligible participants; 38.7% women, 60.7% men). Significantly fewer women were married (64.3% vs. 92% of men, p < 0.001), and significantly more were childless (21.4% of women vs. 9.1% of men, p = 0.037). More men reported exclusive/major responsibility for five duties, including yard work and home maintenance (all p < 0.03). More women reported exclusive/major responsibility for 15 duties, including meal planning and coordinating childcare (all p < 0.03). Women had higher Emotional Exhaustion on univariate analysis (p = 0.015). Across 27 interviews, two main themes were identified, each with three associated subthemes: Theme one, "division of duties," with subthemes (1) the way household duties were divided, (2) traditional gender norms, and 3. changing duties over time/unexpected circumstances. Theme two, "impact of domestic duties," with subthemes (1) professional, (2) financial, and (3) burnout/life satisfaction. CONCLUSIONS: Women OHNS disproportionately manage domestic responsibilities, possibly altering career trajectory for some OHNS. Burnout, especially emotional exhaustion, may be elevated due to inequitable labor. Future research should focus on identifying ways to improve equity for this group. LEVEL OF EVIDENCE: N/A Laryngoscope, 134:S1-S12, 2024.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".