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Record W4381489326 · doi:10.2166/washdev.2023.001

Building a business case for workplace menstrual hygiene management programs: a social cost–benefit analysis

2023· article· en· W4381489326 on OpenAlexaff
Jacob Eaton, Michelle Bronsard, Mark Radin, Christopher Kaunda, Michal Avni, Aditi Krishna, Mary Kincaid

Bibliographic record

VenueJournal of Water Sanitation and Hygiene for Development · 2023
Typearticle
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsUniversité de Montréal
FundersUnited States Agency for International Development
KeywordsSanitationHygieneEmpowermentIntervention (counseling)ProductivityAttendanceBusinessEnvironmental healthPsychologyNursingMedicineEconomic growthEconomics

Abstract

fetched live from OpenAlex

Abstract Inadequate menstrual health and hygiene (MHH) pose a great challenge for working women, affecting their productivity, job satisfaction, attendance, and advancement, and also have implications for their employers. Yet there has been little research to quantify the impacts of poor MHH conditions or to consider the value add of workplace MHH programs. As part of USAID's Water Sanitation and Hygiene Partnerships for Learning and Sustainability project, we conducted a social cost–benefit analysis (CBA) of the Menstrual Hygiene Management (MHM) in the Workplace Action Research, a 10-month intervention in private sector enterprises in Nepal and Kenya. The intervention aimed to determine if providing adequate MHM in the workplace contributes to women's economic empowerment, including improved business and social outcomes. This CBA of a workplace MHM intervention – the first of its kind – found a positive return for investing in workplace MHM programs. The average benefit–cost ratio in the base-case across factories in a 10-month intervention was 1.4, which increased to 2.3 when projected over 24 months. These early results of a pilot CBA on MHM in the workplace should serve as a call for greater attention by governments and businesses to the needs of menstruating women.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.049
GPT teacher head0.344
Teacher spread0.295 · 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 designOther design
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
Published2023
Admission routes1
Has abstractyes

Explore more

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