Stories From the Field: The Use of Information and Communication Technologies to Address the Health Needs of Underserved Populations in Latin America and the Caribbean
Bibliographic record
Abstract
BACKGROUND: As their availability grew exponentially in the last 20 years, the use of information and communication technologies (ICT) in health has been widely espoused, with many emphasizing their potential to decrease health inequities. Nonetheless, there is scarce availability of information regarding ICT as tools to further equity in health, specifically in Latin American and Caribbean settings. OBJECTIVE: Our aim was to identify initiatives that used ICT to address the health needs of underserved populations in Latin America and Caribbean. Among these projects, explore the rationale behind the selection of ICT as a key component, probe perceptions regarding contributions to health equity, and describe the challenges faced during implementation. METHODS: We conducted an exploratory qualitative study. Interviews were completed via Skype or face-to-face meetings using a semistructured interview guide. Following participant consent, interviews were audio recorded and verbatim transcriptions were developed. All transcriptions were coded using ATLASti7 software. The text was analyzed for patterns, shared themes, and diverging opinions. Emerging findings were reviewed by all interviewers and shared with participants for feedback. RESULTS: We interviewed representatives from eight organizations in six Latin American and Caribbean countries that prominently employed ICT in health communication, advocacy, or surveillance projects. ICT expanded project's geographic coverage, increased their reach into marginalized or hard-to-reach groups, and allowed real-time data collection. Perceptions of contributions to health equity resided mainly in the provision of health information and linkage to health services to members of groups experiencing greater morbidity because of poverty, remote place of residence, lack of relevant public programs, and/or stigma and discrimination, and in more timely responses by authorities to the health needs of these groups as a result of the increased availability of strategic information on morbidity and its social determinants. Most projects faced initial resistance to implementation because of lack of precedents. Their financial and technical sustainability was threatened by reliance on external funding and weak transitional structures amidst key staff changes. Projects often experienced challenges in establishing meaningful communication with target audience members, mainly because of divergent motivations behind ICT use between projects and its target audience and the lack of access or familiarity with ICT among the most underserved members of such audiences. CONCLUSIONS: ICT can benefit projects focusing on the health needs of underserved populations by expanding the breadth and depth of target audience coverage and improving data management. Most projects tended to be small, short-term pilot interventions with limited engagement with the formal health sector and did not include health equity as an explicit component. Collaborative projects with government institutions, particularly those with health surveillance objectives, seemed to be the most optimistic about long-term sustainability.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".