Reducing childhood mortality in poor countries
Bibliographic record
Abstract
The persistence of high perinatal and neonatal mortality rates in many developing countries make efforts to improve perinatal care in the home and at local health facilities important public health concerns. We describe a study which aims to evaluate a community-level participatory intervention in rural Nepal. The effectiveness of community-based action research interventions with mothers and other key members of the community in improving perinatal health outcomes is being examined using a cluster randomized, controlled trial covering a population of 28,000 married women of reproductive age. The unit of randomization was the village development committee (VDC): 12 VDCs receive the intervention while 12 serve as controls. The key elements of the intervention are the activities of female facilitators, each of whom works in one VDC facilitating the activities of women's groups in addressing problems in pregnancy, childbirth and the newborn period. Each group moves through a participatory planning cycle of assessment, sharing experiences, planning, action and reassessment, with the aim of improving essential maternal and newborn care. Outcomes assessed are neonatal and perinatal mortality rates, changes in patterns of home care, health care seeking and referral. The study also aims to generate programmatic information on the process of implementation in communities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".