Digital health and the promise of equity in maternity care: A mixed methods multi-country assessment on the use of information and communication technologies in healthcare facilities in Latin America and the Caribbean
Bibliographic record
Abstract
INTRODUCTION: Timely access to maternity care is critical to saving lives. Digital health may serve to bridge the care chasm and advance health equity. Conducted in the aftermath of the COVID-19 pandemic, this cross-sectional mixed-methods study assessed the use of information and communication technologies (ICTs) in healthcare facilities in nine Latin American and Caribbean countries to understand the landscape of ICT use in maternity care and the barriers and facilitators to its adoption. MATERIALS AND METHODS: Between April 2021 and September 2022, we disseminated an online survey in English and Spanish among, mainly public, healthcare institutions that provided maternity care in Argentina, Bolivia, Colombia, the Dominican Republic, Ecuador, Guyana, Honduras, Paraguay and Peru. We also interviewed 27 administrators and providers in ministries of health and healthcare institutions. RESULTS: Most of the 1877 institutions that answered the survey reported using ICTs in maternity care (N = 1536, 82%), ranging from 96% in Peru to 64% in the Dominican Republic. Of institutions that used ICTs, 59% reported using them more than before or for the first time since the pandemic began. ICTs were most commonly used to provide family planning (64%) and breastfeeding (58%) counseling, mainly by phone (82%). At the facility level, availability of equipment and internet coverage, coupled with skilled human resources, were the main factors associated with ICT use. At country level, government-led initiatives to develop digital health platforms, alongside national investments in the digital infrastructure, were the determining factors in the adoption of ICTs in healthcare provision. CONCLUSION: Digital health for maternity care provision relied on commonly available technology and did not necessitate highly sophisticated systems, making it a sustainable and replicable strategy. However, disparities in access to digital health remain and many facilities in rural and remote areas lacked connectivity. Use of ICTs in maternity care depended on countries' long-term commitments to achieving universal health and digital coverage.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".