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Record W4293081391 · doi:10.1371/journal.pgph.0000405

Navigating fear, shyness, and discomfort during menstruation in Cambodia

2022· article· en· W4293081391 on OpenAlexaff
Gabrielle Daniels, Marin MacLeod, Raymond E. Cantwell, Danya E. Keene, Debbie Humprhies

Bibliographic record

VenuePLOS Global Public Health · 2022
Typearticle
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersSamaritan's Purse
KeywordsShynessMenstruationPsychologyMedicineDevelopmental psychologyPsychiatryAnxietyInternal medicine

Abstract

fetched live from OpenAlex

While increased attention has been given to girls' menstrual hygiene management (MHM) experiences in schools as they relate to managerial challenges, research exploring girls' psychosocial experiences during menstruation and their needs in non-school environments remains limited. This study investigates the knowledge, attitudes, and practices regarding menstruation and MHM (M&MHM) among rural Cambodian girls (at least 14 years old, post-menarche; n = 130), mothers (n = 93), fathers (n = 15), teachers (n = 37; 54.1% female), and boys (at least 14 years old; n = 59) in both the home and school environments. Qualitative and quantitative data were collected through structured interviews, focus groups, and latrine surveys in eight secondary schools and villages from two rural provinces, Banteay Meanchey and Kratie. Findings indicated that although 95% of girls felt capable of managing their menses each month, many girls experienced fear, shyness, and discomfort (FSD) during menstruation. Identified M&MHM challenges and FSD in both the home and school environments influenced all participant groups' decision-making, social interactions, and varied based on their knowledge of M&MHM and emphasized the need for comprehensive interventions that reduce the impact of MHM challenges on psychosocial experiences and FSD to promote girls' well-being during menstruation, particularly in income limited settings.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.050
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.349
Teacher spread0.307 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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