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Enregistrement W4400084851 · doi:10.1093/eurjpc/zwae217

The cardiometabolic consequences of workplace sexual harassment

2024· letter· en· W4400084851 sur OpenAlexaff
Marjan Walli-Attaei, Jackie Bosch

Notice bibliographique

RevueEuropean Journal of Preventive Cardiology · 2024
Typeletter
Langueen
DomaineSocial Sciences
ThématiqueWorkplace Violence and Bullying
Établissements canadiensMcMaster UniversityPopulation Health Research Institute
Organismes subventionnairesBayer
Mots-clésMedicineHarassmentEnvironmental healthNursing

Résumé

récupéré en direct d'OpenAlex

This editorial refers to ‘Exposure to workplace sexual harassment and risk of cardiometabolic disease: a prospective cohort study of 88 904 Swedish men and women’, by P. KC et al., https://doi.org/10.1093/eurjpc/zwae178. For many years, we have known nine risk factors that account for over 90% of the population attributable risk of cardiovascular (CV) disease.1 Many programmes have been developed to identify and treat eight of these risk factors: hypertension, diabetes, abnormal lipids, abdominal obesity, nutrition, physical activity, alcohol consumption, and tobacco use, with varying levels of success.2,3 However, the ninth risk factor, psychosocial factors—including depression, locus of control, and stress—is rarely considered. Despite advancements in understanding the underlying pathophysiological mechanisms of stress,4 programmes for its assessment and intervention lag far behind those of other CV risk factors. Workplace stress, in particular, has been associated with both cardiovascular disease5–7 and mental health7 issues, yet is rarely considered. The study by KC et al.8 highlights a particular element of workplace stress: sexual harassment, identifying it as a potentially important consideration for CV risk. The authors examined the associations of workplace sexual harassment—defined in this study as undesirable advances or offensive references to what is generally associated with sexual relations—with incident cardiovascular disease (CVD) and type-2 diabetes in nearly 89 000 Swedish workers followed for 11 years. Using responses to the Swedish Work Environment Survey (SWES) from 1995 to 2015, linked with National Patient Register and Causes of Death Register, the authors found that 1.9% of men and 7.5% of women reported sexual harassment in the workplace. After adjusting for sociodemographic factors (e.g. sex, birth country, family situation, education, and income) and work-related factors (e.g. job demands, job control, job support, and physical strain at work), the researchers found that workplace sexual harassment was associated with an increased risk of incident CVD [hazard ration (HR) 1.25, 95% confidence interval (CI) 1.03–1.51], and type 2 diabetes [HR 1.45, 95% CI 1.21–1.73]. The study also highlighted that the frequency of exposure to harassment and the type of harasser played a crucial role in determining the risk. Sexual harassment by a supervisor or fellow worker was associated with a higher risk of CVD (HR 1.57, 95% CI 1.15–2.15) and type-2 diabetes (HR 1.85, 95% CI 1.39–2.46) compared to harassment by others. Frequent exposure to harassment further suggested a potential increased risk, with HRs of 1.31 (95% CI 0.95–1.81) for CVD and 1.72 (95% CI 1.30–2.28) for type-2 diabetes. While the prevalence of workplace sexual harassment was higher among women, the authors did not find statistically significant interactions between workplace sexual harassment and biological sex. This study highlights the significant health impacts of workplace sexual harassment. The strengths of the study include the long-term follow-up and exploration of potential dose-relationships by examining associations with the type of harasser and frequency of harassment. The importance of establishing the long-term impact of workplace sexual harassment cannot be understated. In Europe, ∼9% of women report experiencing sexual harassment at work and a similar proportion of National Health Service workers in the UK reported workplace sexual harassment.9,10 Though these figures likely underestimate the real scope of the issue given the sensitive nature of sexual harassment, along with fear of retaliation, stigma, and perceived lack of support systems in the workplace. There are two key issues that remain unclear: (i) whether the associations reported by the authors are direct or mediated through secondary mechanisms, and (ii) if they are direct, what can be done to address this? It is plausible that workplace sexual harassment increases the likelihood of deleterious behaviours such as smoking, alcohol consumption, unhealthy diet choices, and physical inactivity. Future research should explore whether there is an independent association between workplace sexual harassment and incident CVD and type-2 diabetes, apart from these behavioural/lifestyle risk factors. In the meantime, CV risk factor screening programmes should include assessment of workplace stress and explicitly inquire about workplace sexual harassment. This, of course, implies that the employers need to be prepared to address these issues if identified. There are limitations to this study. The authors did not account for traditional metabolic risk factors, including systolic blood pressure, dyslipidaemia, elevated blood pressure, and abdominal obesity, which may have confounded the associations. Irrespective of the precise mechanisms, this study, along with the broader literature on the detrimental effects of workplace sexual harassment on health and well-being, should serve as a clarion call for employers across all sectors to better understand and take decisive actions against misconduct in the workplace. By implementing preventative measures, explicitly addressing and providing comprehensive support systems, employers can significantly reduce—or even prevent—sexual harassment in the workplace. These actions will not only promote a more equitable environment but may also contribute to the health of workers, at least in part due to cardiovascular risk factor reduction. J.B. received funding for event adjudication from Bayer AG outside of the current work. No new data were generated or analysed in support of this article.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,018
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,019

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,018
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0030,002
Science ouverte0,0020,001
Intégrité de la recherche0,0200,018
Charge utile insuffisante (le modèle a refusé de juger)0,0060,006

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.

Tête enseignante Opus0,029
Tête enseignante GPT0,307
Écart entre enseignants0,278 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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Même revueEuropean Journal of Preventive CardiologyMême sujetWorkplace Violence and BullyingTravaux en français237 207