The Impact of Workplace Harassment on Health in a Working Cohort
Notice bibliographique
Résumé
BACKGROUND: Workplace abuse, including sexual harassment, is frequently experienced worldwide and is related to adverse mental health outcomes and injuries. Flight attendants are an understudied occupational group and are susceptible to harassment due to working in a feminized, client-facing occupation with few protections or sanctioned responses against aggressive behaviors. OBJECTIVE: We investigated the relationship between workplace abuse and health in a cohort of cabin crew. We also aimed to characterize perpetrator profiles. METHODS: We conducted our study among 4,459 U.S. and Canada-based participants from the Harvard Flight Attendant Health Study using multivariate logistic regression. Our exposures of interest were episodes of workplace abuse in the past year. We evaluated several mental and physical health outcomes, including depression, fatigue, musculoskeletal injuries, and general workplace injuries. RESULTS: We report that exposures to verbal abuse, sexual harassment and sexual assault are common among cabin crew, with 63%, 26% and 2% of respondents, respectively, reporting harassment in the past year alone. Workplace abuse was associated with depression, sleep disturbances, and musculoskeletal injuries among male and female crew, with a trend towards increasing odds ratios (ORs) given a higher frequency of events. For example, sexual harassment was related to an increased odds for depression (OR=1.91, 95% confidence interval [CI]: 1.51-2.30), which increased in a dose response-like manner among women reporting harassment once (OR=1.44, 95% CI: 0.93-1.95), 2-3 times (OR=1.83, 95% CI: 1.29-2.38), and 4 or more times (OR=4.12, 95% CI: 3.18-5.06). We found that passengers were the primary perpetrators of abuse. CONCLUSIONS: Our study is the first to comprehensively characterize workplace abuse and harassment and its relation to health in a largely female customer-facing workforce. The strong associations with health outcomes observed in our study highlights the question of how workplace policies can be altered to mitigate prevalent abuses. Clinicians could also consider how jobs with high emotional labor demands may predispose people to adverse health outcomes, educate patients regarding their psychological/physical responses and coping strategies, and be aware of signs of distress in patients working in such occupations in order to direct them to the appropriate treatments and therapies.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».