Practices and Predictors of Menstrual Hygiene Management Material Use Among Adolescent and Young Women in Rural Pakistan. A Cross-Sectional Study.
Bibliographic record
Abstract
Abstract Background In low- and middle-income countries, women often use inappropriate materials to manage menstruation, which can pose a hazard to their health. Inappropriate menstrual hygiene management (MHM) can also have important downstream consequences beyond physiologic health, including the restriction of adolescent girls’ access to academic pursuits. This impacts one’s quality of life and has potential economic consequences for society. Methods Among menstruating adolescent and young women 15-23 years of age living in rural Pakistan (n = 25,305), we aimed to describe MHM practices and generate a predictive model of the socioeconomic and demographic factors related to the use of MHM materials. Beliefs and barriers around MHM were also summarized. The outcome variable included: those who practiced appropriate (reported use of a sanitary pad or/and new piece of cloth) and inappropriate MHM (reported use of an old cloth and/or nothing). Logistic regression was used to generate the predictive model, with results presented as odds ratios (OR) and 95% confidence interval (CI). Results Inappropriate MHM practices were reported by 75% of participants. The majority (61.9%) reported using old cloths, 12.6% used nothing and 0.5% used old cloth with sanitary pad. One fourth of participants reported appropriate MHM material use, including, 16.2% sanitary pads, 8.6% new cloth and a few reported using sanitary pads with new cloth (0.2%). Inappropriate MHM practices were more common in lowest wealth quintile (OR 4.41; 95% CI = 2.77 to 7.01, P<0.0001), followed by those with no education (OR 3.9; 95% CI = 3.36 to 4.52, P<0.0001). Mothers were the primary source of information about menarche (84.5%). Among school-going girls, 22% reported not going to school while menstruating. The affordability of menstrual hygiene products, awareness of appropriate practices, access to clean supplies, and cultural beliefs were identified as factors contributing to MHM practices. Conclusions Findings indicate the need for multi-sectorial efforts to introduce MHM-specific and MHM-sensitive interventions to improve MHM practices, ranging from availability of low-cost MHM materials to the inclusion of MHM education in school curriculums and within community platforms. Trial Registration The trial was registered on ClinicalTrials.gov (Identifier: NCT03287882).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".