Girls’ Menstrual Management in Five Districts of Nepal: Implications for Policy and Practice
Bibliographic record
Abstract
Discriminatory practices related to menstruation affect the social, mental and physical wellbeing of girls in many low-and middle-income countries. We conducted mixed methods research in five districts of Nepal to explore how menstruation affected girls’ ability to fully participate in school and community life. We conducted 860 structured interviews, 26 group interviews and 10 focus group discussions with schoolgirls in rural areas,14 semi-structured interviews with girls’ mothers, and 10 interviews with health teachers. Girls in all districts experienced social, material and information barriers to confident menstrual management. Menstrual blood was believed to carry diseases, and girls’ movement was restricted to contain ritual pollution and protect them from illness, spirit possession, and sexual experiences. Taboos prevented girls from worshipping in temples or in their home, and some girls were not allowed to enter the kitchen, or sleep in their home while menstruating. Teachers and parents felt unprepared to answer questions about menstruation and focused on the maintenance of restrictions. Teachers and students were embarrassed discussing menstruation in school and classes were not question-driven or skills-based. Gender disaggregated teaching of menstruation and engagement of health facility staff may have positive effects. Community participatory approaches that engage girls, their families and the wider community are necessary to address harmful cultural practices. Cross-sectoral approaches to provide clean, private, safe spaces for girls and increased availability of preferred materials could enable confident menstrual management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".