Tackling equitable coverage and quality of care for neonates in hospitals: a pre-post assessment on asphyxia interventions in Mesoamerica
Bibliographic record
Abstract
BACKGROUND: Intrapartum-related hypoxic events, or birth asphyxia, causes one-fourth of neonatal deaths globally and in Mesoamerica. Multidimensional care for asphyxia must be implemented to ensure timely and effective care of newborns. Salud Mesoamérica Initiative (SMI) is a performance-based program seeking to improve maternal and child health for low-income areas of Central America. Our objective was to assess the impact of SMI on neonatal asphyxia care in health centers and hospitals in the region. METHODS: A pre-post design. Two hundred forty-eight cases of asphyxia were randomly selected from medical records at baseline (2011-2013) and at second-phase follow-up (2017-2018) in Mexico (state of Chiapas), Honduras, Nicaragua, and Guatemala as part of the SMI Initiative evaluation. A facility survey was conducted to assess quality of health care and the management of asphyxia. The primary outcome was coverage of multidimensional care for the management of asphyxia, consisting of a skilled provider presence at birth, immediate assessment, initial stabilization, and appropriate resuscitation measures of the newborn. Data were analyzed using multivariable logistic regression. RESULTS: Management of asphyxia improved significantly after SMI. Proper care of asphyxia in intervention areas was better (OR = 2.4; 95% CI = 1.3-4.6) compared to baseline. Additionally, multidimensional care was significantly higher in Honduras (OR = 4.0; 95% CI = 1.4-12.0) than in Mexico. Of the four multidimensional care components, resuscitation showed the greatest progress by follow-up (65.7%) compared to baseline (38.7%). CONCLUSION: SMI improved the care for neonatal asphyxia management across all levels of health care in all countries. Our findings show that proper training and adequate supplies can improve health outcomes in low-income communities. SMI provides a model for improving health care in other 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".