Training programs to improve identification of sick newborns and care-seeking from a health facility in low- and middle-income countries: a scoping review
Bibliographic record
Abstract
BACKGROUND: Most neonatal deaths occur in low- and middle-income countries (LMICs). Limited recommendations are available on the optimal personnel and training required to improve identification of sick newborns and care-seeking from a health facility. We conducted a scoping review to map the key components required to design an effective newborn care training program for community-based health workers (CBHWs) to improve identification of sick newborns and care-seeking from a health facility in LMICs. METHODS: We searched multiple databases from 1990 to March 2020. Employing iterative scoping review methodology, we narrowed our inclusion criteria as we became more familiar with the evidence base. We initially included any manuscripts that captured the concepts of "postnatal care providers," "neonates" and "LMICs." We subsequently included articles that investigated the effectiveness of newborn care provision by CBHWs, defined as non-professional paid or volunteer health workers based in communities, and their training programs in improving identification of newborns with serious illness and care-seeking from a health facility in LMICs. RESULTS: Of 11,647 articles identified, 635 met initial inclusion criteria. Among these initial results, 35 studies met the revised inclusion criteria. Studies represented 11 different types of newborn care providers in 11 countries. The most commonly studied providers were community health workers. Key outcomes to be measured when designing a training program and intervention to increase appropriate assessment of sick newborns at a health facility include high newborn care provider and caregiver knowledge of newborn danger signs, accurate provider and caregiver identification of sick newborns and appropriate care-seeking from a health facility either through caregiver referral compliance or caregivers seeking care themselves. Key components to consider to achieve these outcomes include facilitators: sufficient duration of training, refresher training, supervision and community engagement; barriers: context-specific perceptions of newborn illness and gender roles that may deter care-seeking; and components with unclear benefit: qualifications prior to training and incentives and remuneration. CONCLUSION: Evidence regarding key components and outcomes of newborn care training programs to improve CBHW identification of sick newborns and care-seeking can inform future newborn care training design in LMICs. These training components must be adapted to country-specific contexts.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 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".