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Record W1882959273 · doi:10.2196/publichealth.4108

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

2015· article· en· W1882959273 on OpenAlexaffvenue
Nasim Farach, Gladys Faba, Báltica Cabieses, Marcelo D’Agostino, Andrea Cortinois

Bibliographic record

VenueJMIR Public Health and Surveillance · 2015
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsLatin AmericansInformation and Communications TechnologyCaribbean regionEquity (law)Health equityEconomic growthHealth communicationEnvironmental healthPublic relationsPolitical scienceMedicineBusinessHealth careComputer scienceWorld Wide WebEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.595
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.224
GPT teacher head0.429
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2015
Admission routes2
Has abstractyes

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