Work, Gender, and Sexual Harassment on the Frontlines of Commercial Travel: A Cross-Sectional Study of Flight Crew Well-Being
Bibliographic record
Abstract
Background The scale and scope of experience of sexual harassment at work among male and female flight attendants was examined. Flight attendants are a predominantly female workforce with an occupational heritage of female and male sexualized stereotypes.Method Data represented perception and prevalence of sexual harassment related to hostile work environment among 8,700 North American (U.S. and Canada) and 1,887 United Kingdom (UK) flight attendants in the Flight Attendant Health Study.Results Sexual harassment, mostly from passengers, pilots, and coworkers, was reported by 26% of North American flight attendants and 11% of UK flight attendants. Yet, 61% of UK flight attendants experienced unwanted sexual behaviors, pointing to possible underreporting of the sexual harassment problem. Sexually offensive behaviors received variable labeling as sexual harassment depending on gender of the victim and perpetrator profile.Conclusion Sexual harassment is a potentially underreported workplace problem for flight attendants with underreporting resulting from gender-specific differences in individual perceptions. Understanding these differences is vital to addressing sexual harassment and the concomitant health risks. Traditional client and customer attitudes might lead to rose-colored victims’ judgment, especially in the case of customer service jobs involving emotional labor. Our results provide new information to guide future research regarding well-being of this understudied group of service employees.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".