The <i>2018 Inter-agency field manual on reproductive health in humanitarian settings</i>: revising the global standards
Bibliographic record
Abstract
Since the 1990s, the Inter-agency field manual on reproductive health in humanitarian settings (IAFM) has provided authoritative guidance on reproductive health service provision during different phases of complex humanitarian emergencies. In 2018, the Inter-Agency Working Group on Reproductive Health in Crises will release a new edition of this global resource. In this article, we describe the collaborative and inter-sectoral revision process and highlight major changes in the 2018 IAFM. Key revisions to the manual include repositioning unintended pregnancy prevention within and explicitly incorporating safe abortion care into the Minimum Initial Service Package (MISP) chapter, which outlines a set of priority activities to be implemented at the outset of a humanitarian crisis; stronger guidance on the transition from the MISP to comprehensive sexual and reproductive health services; and the addition of a logistics chapter. In addition, the IAFM now places greater and more consistent emphasis on human rights principles and obligations, gender-based violence, and the linkages between maternal and newborn health, and incorporates a diverse range of field examples. We conclude this article with an outline of plans for releasing the 2018 IAFM and facilitating uptake by those working in refugee, crisis, conflict, and emergency settings.
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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 | 0.077 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".