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Record W4306660023 · doi:10.11591/ijphs.v11i4.21938

Menstrual hygiene practices among adolescent schoolgirls in the rural area of Bangladesh

2022· article· en· W4306660023 on OpenAlexaff
Mst. Rokshana Rabeya, Md. Nazrul Islam, Umme Kulsum Hafsa, Nadiatul Ami Nisa, Gopal Kumar Ghosh, Afsana Yesmin, Khairun Nahar Juthi, Tamima Rahman

Bibliographic record

VenueInternational Journal of Public Health Science (IJPHS) · 2022
Typearticle
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHygieneMedicineMenarcheEnvironmental healthLogistic regressionCross-sectional studyDemographyFamily medicinePediatrics

Abstract

fetched live from OpenAlex

<div align="center">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</div>

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.106
GPT teacher head0.415
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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