‘In this era, the digital era, telehealth is a tool that we can use to provide specialized care’: a qualitative evaluation of the implementation of programmes that use digital health to improve antenatal care in rural and jungle areas of Honduras and Peru
Bibliographic record
Abstract
Introduction: Antenatal care is essential to save lives, yet large disparities in access persist. Leveraging information and communications technologies (ICTs) to deliver maternal care in hard-to-reach areas offers a promising strategy to close this gap. However, integrating ICTs into healthcare systems can be challenging. This qualitative evaluation sought to identify key implementation factors associated with integrating ICTs to improve maternal care in jungle and rural areas. Methods: Between April and June 2024, we conducted 18 semi-structured interviews guided by the Consolidated Framework for Implementation Research to evaluate two programmes implemented in Santa Bárbara, Honduras, and Amazonas, Peru, between 2022 and 2023. A purposive sample of key informants, comprising technical personnel, administrators, and healthcare providers, was recruited to represent staff at all levels engaged in decision-making and programme implementation. Results: Interviewees perceived that the interventions were feasible and improved the timely detection and management of pregnancy complications. They also reported strengthened interpersonal relationships among healthcare providers and between women and providers. Facilitating factors included the perception that the programmes addressed an urgent need and were aligned with national priorities. In Peru, participants highlighted the national telemedicine platform and robust policy framework regulating telemedicine as important facilitators. In Honduras, they emphasised the programme's adaptability and the use of widely-available technology. Unreliable internet connectivity, weak mobile network signals, overstretched healthcare personnel, and insufficient administrative support for scheduling and coordination were mentioned as barriers. Conclusions: The programmes were perceived as feasible and promising to improve the detection and management of pregnancy complications among women in hard-to-reach areas. Sustained government investment in developing the digital health infrastructure was essential for their adoption and scale-up. A consideration for future programmes is that, while ICTs can enhance the efficiency of healthcare systems, they cannot substitute for an adequate health workforce or essential medical supplies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".