Menstrual hygiene: a ‘silent’ need during disaster recovery
Bibliographic record
Abstract
Post-disaster relief and recovery operations seldom focus on women’s priorities regarding menstrual hygiene. There is an increasing awareness to incorporate inclusive, participatory, and gender-sensitive strategies for implementation of response programmes. This article presents empirical findings related to menstrual hygiene management (MHM), demonstrating it is integral to women’s privacy and safety during recovery. Using case studies from India, the 2012 Assam floods and 2013 Cyclone Phailin in Odisha, this article explores menstrual hygiene practices in a post-disaster context. The data were collected through participatory learning and action tools such as focus group discussions, household interviews, priority ranking, and observations. It emerged that menstrual hygiene was overlooked at the household level during recovery; women and adolescent girls faced seclusion and isolation, exacerbating privacy and security concerns post-disasters. Some humanitarian agencies have an ad hoc approach towards MHM, which is limited to distribution of sanitary pads and does not address the socio-cultural practices around MHM. There is a need for strategic planning to address MHM with a gender-sensitive and inclusive approach. This article draws practical and policy inferences from the research for stronger approaches towards initiating behaviour change in MHM, and addressing attitudes and knowledge regarding menstrual hygiene.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".