Menstrual-related symptoms and absence from school among young people in Sweden: a stratified, randomized, population-based survey
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.618 | 0.002 |
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; both teacher heads agree on what is shown here.
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".