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Record W4394853067 · doi:10.1101/2024.04.12.24305724

Menstrual hygiene management in two districts of Malawi

2024· preprint· en· W4394853067 on OpenAlexfundno aff
Rebekah Hinton, Laurent-Charles Tremblay-Lévesque, Modesta Kanjaye, C. J. A. Macleod, Mads Troldborg, Robert M. Kalin

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsnot available
FundersGlobal Affairs CanadaGovernment of CanadaJames Hutton Institute
KeywordsHygieneBusinessGeographyEnvironmental healthSocioeconomicsMedicineEconomics

Abstract

fetched live from OpenAlex

1 Abstract Menstrual hygiene management (MHM) forms a critical component of ensuring access to adequate and equitable sanitation for all, as outlined in SDG 6.2. Despite its importance, little is known about MHM in Malawi, particularly at a household level. Through a household survey of MHM within 2 districts, we evaluated the type of menstrual absorbents used by people who menstruate. Reusable cloths/rags were the most used menstrual absorbent, used by 79.5% of respondents, whilst disposable absorbents, such as tampons and sanitary pads, were used by 18.6% of respondents. Appropriate MHM also incorporates adequate management of MHM materials, including the washing and drying of reusable menstrual absorbents. We evaluated the cleaning of reusable menstrual absorbents; most respondents (90.1%) reported appropriate washing of menstrual absorbents using soap and water, however only 20.3% reported that menstrual absorbents were dried outside in the sun (as is best practise) with most reporting that reusable menstrual absorbents were dried inside their homes. Our findings highlight the need for improved MHM within Malawi, not only in the access and affordability of appropriate menstrual absorbents but also the promotion of appropriate washing and drying of menstrual absorbents.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.033
GPT teacher head0.361
Teacher spread0.328 · 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.

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
Published2024
Admission routes1
Has abstractyes

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