Menstrual cycle-associated symptoms and workplace productivity in US employees: a cross-sectional survey of users of the Flo mobile phone app
Notice bibliographique
Résumé
Background: Mood and physical symptoms related to the menstrual cycle greatly affect women’s productivity at work and often lead to absenteeism. However, employer-led initiatives to tackle these issues are lacking. Digital health interventions focused on women’s health (such as the Flo mobile phone app) could help fill this gap given their anonymity, scalability and accessibility. Objective: We aimed to 1) measure the impact of disturbances related to the menstrual cycle on work-related productivity in users of the Flo app; 2) characterize levels of support and benefits women receive in the workplace and 3) explore whether the Flo app could help mitigate the impact of issues related to the menstrual cycle on productivity and absenteeism. Methods: 1867 users of the Flo app participated in a survey exploring 1) the extent to which their menstrual cycle negatively impacts their workplace productivity, including whether menstrual cycle-related symptoms led to absence from work in the previous 12 months; 2) whether Flo users feel supported by their manager and whether they receive specific benefits regarding issues related to their cycle and 3) the role of Flo in the management of menstrual cycle symptoms, preparedness, bodily awareness, openness with others, perceived support and mood.Results: The majority of Flo users reported a moderate to severe impact of their cycle on workplace productivity. 45.2% of the respondents reported absenteeism, with an average of 5.8 days of work missed due to their cycle. 48.4% reported not receiving any support from their manager and 94.6% said they were not provided with any specific benefit for issues related to their menstrual cycle. 75.6% declared they want such benefits. Users stated that the Flo app helped them with the management of menstrual cycle symptoms (68.7%), preparedness and bodily awareness (88.7%), openness with others (52.5%) and feeling supported (77.6%). Furthermore, users who reported the most positive impact of the Flo app were 18-25% less likely to report an impact of their menstrual cycle on their productivity and 12-18% less likely to take days off work for issues related to their cycle.Conclusions: The menstrual cycle can have a significant effect on workplace productivity and absenteeism, and resources available to employees are scarce. Digital health apps, such as the Flo app, could equip individuals with tools to better cope with issues related to their menstrual cycle and facilitate discussions around menstrual health in the workplace.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».