Workplace menstrual health in the private sector: Results from a pilot study in Kenya and Nepal
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".