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Record W4400189360 · doi:10.2166/washdev.2024.026

Workplace menstrual health in the private sector: Results from a pilot study in Kenya and Nepal

2024· article· en· W4400189360 on OpenAlexaff
Aditi Krishna, Dhruhini Maneshka Eliatamby, M. Whitney Fry, Aishwarya Nagar, Jacob Eaton, Michelle Bronsard, Joan W. Njagi, Alfred Muli, Sheila Mutua, Anjana Dongol, Prakash Luitel, Meena Sharma, Sunita Raut, Mary Kincaid, Michal Avni

Bibliographic record

VenueJournal of Water Sanitation and Hygiene for Development · 2024
Typearticle
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSanitationPsychological interventionEmpowermentWomen's empowermentPrivate sectorPsychologyEnvironmental healthBusinessNursingMedicineEconomic growth

Abstract

fetched live from OpenAlex

ABSTRACT Menstrual health (MH) efforts have not focused on challenges experienced by working adults. For individuals who identify as women, managing one's periods outside the home is especially difficult when working in male-dominated workplaces. In response, USAID Water, Sanitation, and Hygiene Partnerships for Learning and Sustainability implemented four workplace interventions in Kenya and Nepal to improve MH conditions, promote women's economic empowerment, and garner support from company leadership for workplace MH programs. Over 9–11 months, interventions focused on (i) menstrual products and WASH infrastructure; (ii) workplace policy environment; and (iii) education and behavior change. Pre–post, mixed methods evaluations revealed that awareness and confidence regarding MH increased in all workplaces. Improved access to menstrual products increased women's comfort and lowered anxiety. In both countries, improved toilets and reduction of supervisory barriers to toilet use during working hours helped women employees to change products regularly. Changing the social and institutional workplace environments through policy recommendations, education and behavior change efforts increased social support and reduced menstruation-related stigma, leading to improved work performance and job satisfaction. Our findings demonstrate the feasibility of implementing workplace MH programs and improving working conditions for menstruating employees in pursuit of economic empowerment and better business outcomes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.057
GPT teacher head0.335
Teacher spread0.277 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

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