TulaSalud: An m-health system for maternal and infant mortality reduction in Guatemala
Bibliographic record
Abstract
The Guatemalan NGO (Non-Governmental Organization) TulaSalud has implemented an m-health project in the Department of Alta Verapaz. This Department has 1.2 million inhabitants (78% living in rural areas and 89% from indigenous communities) and in 2012, had a maternal mortality rate of 273 for every 100,000 live births. This m-health initiative is based on the provision of a cell phone to community facilitators (CFs). The CFs are volunteers in rural communities who perform health prevention, promotion and care. Thanks to the cell phone, the CFs have become tele-CFs who able to carry out consultations when they have questions; send full epidemiological and clinical information related to the cases they attend to; receive continuous training; and perform activities for the prevention and promotion of community health through distance learning sessions in the Q'eqchí and/or Poqomchi' languages. In this study, rural populations served by tele-CFs were selected as the intervention group while the control group was composed of the rural population served by CFs without Information and Communication Technology (ICT) tools. As well as the achievement of important process results (116,275 medical consultations, monitoring of 6,783 pregnant women, and coordination of 2,014 emergency transfers), the project has demonstrated a statistically significant decrease in maternal mortality (p < 0.05) and in child mortality (p = 0.054) in the intervention group compared with rates in the control group. As a result of the telemedicine initiative, the intervention areas, which were selected for their high maternal and infant mortality rates, currently show maternal and child mortality indicators that are not only lower than the indicators in the control area, but also lower than the provincial average (which includes urban areas).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".