Changes in the menstrual hygiene management facilities and usage among Bangladeshi school girls and its effect on school absenteeism from 2014 to 2018
Bibliographic record
Abstract
BACKGROUND: The lack of menstrual hygiene management (MHM) information and facilities in schools is a major contributor to adolescent girls' school absenteeism in low- and middle-income countries like Bangladesh. OBJECTIVES: This paper examines the changes over time in school MHM facilities, knowledge and perceptions among adolescent girls, in relation to school absenteeism between 2014 and 2018 in Bangladesh. METHODS: We examined changes in MHM and school absenteeism among schoolgirls using nationally representative data from the Bangladesh National Hygiene Baseline Survey 2014 and National Hygiene Survey 2018. Given the repetitive nature of our data and its clustering within participants, our method included performing descriptive analysis, bivariate analysis, and multivariate Generalised Estimating Equation (GEE) modelling to analyse these changes. RESULTS: Results showed that adolescent girls' menstruation-related absenteeism decreased between 2014 and 2018. Percentage of adolescents who missed school decreased from 25% to 14% (PD: -11; CI: -16 to -6.1), while the average number of missed days reduced from 2.8 to 2.5 (PD: -0.33; CI: -0.57 to -0.10). In the GEE model, we found that living in rural areas (coef: -5.6; CI: -10.06 to -1.14), parental restrictions on going outside (coef: 4.47; CI: 0.75 to 8.2), education levels of girls (coef: -9.48; CI: -14.17 to -4.79), girl's belief that menstruation affects school performance (coef: 23.32; CI: 19.71 to 26.93), and using old cloths (coef: -4.2; CI: -7.6 to -0.79) were significantly associated with higher absenteeism. However, participant's age, type of school, knowledge of menstruation before menarche, receiving information regarding MHM, separate place for changing absorbents, and separate latrine and urine facility were not significantly associated with the changes in absenteeism over time. CONCLUSION: This paper emphasised the associations between changes in school absenteeism, parental restrictions on students, students' education levels, and menstruation-related misperceptions. Ongoing research, policy reviews, and targeted interventions to improve MHM perceptions among girls are required to provide long-term benefits for adolescent girls in Bangladesh.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".