Menstrual-related symptoms and absence from school among young people in Sweden: a stratified, randomized, population-based survey
Notice bibliographique
Résumé
Abstract Background Menstrual-related symptoms such as menstrual pain and heavy bleeding impact individuals’ health, quality of life and can limit the ability to engage in daily life activities, including school. Menstrual-related symptoms thus risk reinforcing existing gender inequalities in health among young people, making it an issue of equal rights and public health concerns. No previous study has estimated the prevalence of menstrual-related symptoms and subsequent school absences in Sweden by using population-based data. Methods The study aimed to estimate the prevalence of menstrual-related symptoms and school absence among young people aged 16–29 in Sweden, and to examine associations between symptoms, absence, and sociodemographic factors. A sample (n = 5,483) of individuals aged 16–29 was drawn from a population-based cross-sectional study which used stratified random sampling. We used logistic regression to test sociodemographic factors associated with school absence due to menstrual-related symptoms. Results Menstrual-related symptoms were reported by most of the respondents (91.43%). Menstrual pain was reported by 76.59%, mood changes by 75.70%, ‘other’ menstrual complaints by half (57.88%) and heavy bleeding by 40.14%. Furthermore, 13.70% in total and 19.93% among those aged 16–19 reported that school absence because of menstrual symptoms occurred on every menstruation. Foreign-born individuals and Swedish-born individuals with two foreign-born parents had higher odds of reporting school absence due to menstrual-related symptoms, as did those with parents with short education and those with long-term health issues. ‘Other’ menstrual complaints (such as headache, tiredness and concentration difficulties) had the greatest impact on school absence. Discussion Menstrual-related symptoms are widespread among young people in Sweden. The subsequent absence from school is unevenly distributed according to the individual’s origin, parental education and long-term health issues and should be seen as an issue of gender equity and public health concern. Given the importance of schools for learning and development, student health services need to be equipped with screening methods and referral routines. Further studies should focus on socioeconomic inequities in menstrual health, with a particular focus on young migrants and second-generation immigrants.
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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 | 0,001 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| 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 | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,618 | 0,002 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».