Integrating Indigenous Maya practices and digital health tools to improve outcomes for Indigenous newborns in Guatemala: a community-based initiative
Bibliographic record
Abstract
Neonatal mortality remains a significant equity issue in rural Indigenous communities of Guatemala, where structural barriers and systemic discrimination impede access to quality newborn care. This field report describes a novel community-based initiative implemented by Maya Health Alliance, an Indigenous-lead NGO, to address high neonatal mortality in Maya Kaqchikel communities through a quality improvement (QI) framework. The intervention centers on home-based neonatal care delivered by trained Neonatal Technicians (NTs), supported by a co-designed smartphone application enabling early identification of neonatal danger signs, clinical decision-making, and data collection. The initiative also leverages a culturally responsive referral and patient navigation system to overcome humanistic barriers to care. Designed using QI methodology, the project applies iterative cycles to track key performance indicators such as perinatal and neonatal mortality rates, referral success rates, and the proportion of newborns receiving timely home evaluations. Since launching in 2024, the program has reached 85% of reported newborns, increased referral rates, and engaged local midwives and health staff through ongoing training and co-design efforts. However, challenges have emerged, including high prevalence of low birth weight, limitations in local hospital capacity, and discriminatory care at facilities that discourage families from accepting referrals. The intervention centers Indigenous practices by positioning TMMs at the frontline and adapting protocols to the communities' lived realities. This initiative demonstrates the potential for culturally embedded, digitally supported, and equity-focused QI interventions to improve neonatal outcomes in resource-limited Indigenous settings. Future efforts will focus on expanding staff capacity, deepening community trust, and strengthening health system partnerships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".