From stigma to solutions: harnessing local wisdom to tackle harms associated with menstrual seclusion ( <i>chhaupadi</i> ) in Nepal
Bibliographic record
Abstract
, impose restrictive norms affecting women's daily lives. Chhaupadi is a tradition that involves isolating women and girls during menstruation and after childbirth, along with following other restrictions, which have physical and mental health implications. To date, interventions have yet to fully and sustainably address harms associated with chhaupadi across the country. This two-phase study conducted in Dailekh, Nepal facilitated the development of community-created solutions to mitigate chhaupadi's adverse impacts on women's health. Using Human Centred Design and a community-engaged approach, the discovery phase identified key stakeholders and contextualised chhaupadi, while the subsequent design phase facilitated the development of five community-created interventions. These included leveraging female community health volunteers (FCHVs) for counselling and awareness, targeting mothers to drive behavioural change, engaging the wider community in behaviour change efforts, empowering fathers to catalyse change at home, and training youth for advocacy. The FCHV intervention concept was selected as the most promising intervention by the women co-design team, warranting broader exploration and testing. Additionally, while it is imperative for interventions to prioritise tackling deleterious aspects of chhaupadi, interventions must also acknowledge its deep-rooted cultural significance and history and recognise the positive aspects that some women may wish to preserve.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".