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 BackgroundIn 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). ResultsInappropriate 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.ConclusionsFindings 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 RegistrationThe trial was registered on ClinicalTrials.gov (Identifier: NCT03287882).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".