Menstrual hygiene practices among adolescent schoolgirls in the rural area of Bangladesh
Bibliographic record
Abstract
Adolescence is a time of tremendous opportunity. However, inadequate menstrual hygiene habits are related to lower academic achievement and enrollment at school, with possible effects on longer-term socio-economic status and impaired overall quality of life. Therefore, this cross-sectional study was conducted among 422 adolescent schoolgirls in Bangladesh between July 2019 and February 2020 with the aim of examining menstrual hygiene practices. Data indicated that the mean age of menarche in 422 adolescents was 12.71±0.97. According to the data, 47% had well and 53% had poor hygiene practices. In multivariable logistic regression analysis, the educational status of respondents’ mothers at the secondary level [AOR=2.023, 95% CI: 1.159-3.532], fathers at the graduate and above level [AOR=3.150, 95% CI: 0.883-11.238], high level of household income [AOR=2.580, 95% CI: 1.480-4.495], and knowledge about the complication of poor hygiene practice among girls [AOR=2.286, 95% CI: 1.160-4.504] were significantly associated with the level of hygiene practices. Poor menstrual hygiene practice was found among more than half of girls. Attitude toward safe menstrual materials should initiate to improve good hygiene practices. Awareness campaigns for parents and teachers to assist their children would be a vital strategy to ensure good hygiene practices
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".