Bibliographic record
Abstract
Background: The potential for mobile health has increasingly received attention from global health researchers, but the process of scaling-up mHealth in low- and middle-income countries has not been adequately explored in the literature. The objective of this study was to investigate the lessons learned from scaling up mHealth initiatives in low-and middle-income countries, drawing on the experiences of TulaSalud in Alta Verapaz, Guatemala and case studies from within the literature. Methodology: Informal stakeholder interviews were conducted with community facilitators, officials from the Health Department of Alta Verapaz, and TulaSalud staff and coordinators. Case studies from within the literature were then used to compare the experience of telemedicine in Alta Verapaz to mHealth initiatives in other low- and middle-income countries. Results: The following paper documents TulaSalud’s experience in telemedicine and explores three thematic areas: program successes, challenges, and recommendations. The findings suggest that TulaSalud’s public-private partnerships, constant monitoring and support of community facilitators, as well as the incorporation of videos and photos within the application have all contributed to the program’s successful scale-up. Conversely, the program’s inability to continue providing a monthly stipend for community facilitators, community members’ lack of respect and support for female community facilitators, as well as language barriers, have presented challenges during the program’s scale-up. Conclusion: Based on the literature review and the interviews conducted in Guatemala, mhealth projects seeking to achieve scale should first establish partnerships with various stakeholders; public-private partnerships have been essential to the successful scale-up of mhealth in low- and middle-income countries. Second, mhealth project implementers should establish a gender strategy to reduce the barriers preventing female community health workers from participating in mhealth. Finally, mhealth projects should e designed with end-users in mind.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".