Building resilience for sexual and reproductive health at the community level: learning from three crisis-affected provinces in Pakistan
Bibliographic record
Abstract
Pakistan regularly faces natural disasters and has a longstanding disaster risk management infrastructure. It is also a nation with high maternal and newborn mortality. Rahnuma-Family Planning Association of Pakistan, with support from the US Centers for Disease Control and Prevention, the Women's Refugee Commission and the International Planned Parenthood Federation South Asia Region's Sexual and Reproductive Health Programme in Crisis and Post Crisis Situations Initiative, embarked on building community capacity to prepare for and respond to sexual and reproductive health (SRH) risks in select disaster-prone areas in Pakistan, and linking communities to existing disaster risk management structures at national, regional and district levels.The initiative began with a training of trainers at the national level, which was cascaded to six union councils (UCs) in three districts in Khyber-Pakhtunkhwa, Punjab and Sindh provinces. Participants developed action plans for their respective UCs that addressed gaps in implementing the Minimum Initial Service Package (MISP) for SRH, the international standard of care for SRH in emergency settings. Communities spent 1.5 years implementing their action plans to strengthen their capacity to respond to SRH needs in the event of an emergency.Project learning highlights the benefits of investing in preparedness to strengthen core services and linking communities to existing formal structures. Action planning led to immediate gains and longer-term benefits. The MISP for SRH was integrated into disaster risk management at all levels. Community mobilisation, awareness raising and the creation of blood donor groups and emergency transport contributed to averting mortality at the community level.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".