A qualitative study of adolescent girls’ experiences of menarche and menstruation in rural Tamil Nadu, India
Bibliographic record
Abstract
Background: In low- and middle-income countries, women and girls experience menstrual hygiene management-related health and social challenges such as urinary tract infections, social stigma, and school and workplace absenteeism.Purpose: In this study, we sought to explore how adolescent girls in rural Thirumalaikodi, Tamil Nadu, India experience menarche and menstruation, how their experiences connect to the sociocultural context, and what strategies they use to manage menstruation. This study also informed the adaptation and development of a school-based water, sanitation, and hygiene intervention.Methods: We conducted ten semi-structured qualitative interviews with adolescent girls in ninth standard from June-July 2018. Data were analysed using a thematic network approach.Results: Findings revealed that menarche inaugurates biological transitions of puberty and cultural codes that shape gender norms. Gender norms in turn generate, maintain, and reproduce stigmatizing attitudes, beliefs, and practices that influenced the development of coping mechanisms at home and at school. Resulting adaptations to the intervention consisted of two activities (school lesson and an extracurricular activity) that address knowledge gaps and myths.Conclusions: This study demonstrates the importance of qualitative research in unpacking adolescent girls’ experiences with menarche and menstruation. Study findings also show how formative research can contribute to the adaptation and development of a contextually and culturally-relevant water, sanitation, and hygiene intervention.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".