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Record W4404081793 · doi:10.1080/02681102.2024.2414193

Evaluating a phone-based Interactive Voice Response system for reducing misinformation and improving malaria literacy

2024· article· en· W4404081793 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInformation Technology for Development · 2024
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsYork University
FundersAgence Nationale de la Recherche
KeywordsMisinformationUsabilityLocal languagePhoneLiteracyMobile phoneMalariaComputer scienceHealth literacyInteractive voice responseLanguage barrierMedical educationApplied psychologyInternet privacyPsychologyHealth careMedicineComputer securityPolitical sciencePedagogyTelecommunicationsHuman–computer interactionLinguistics

Abstract

fetched live from OpenAlex

In Burkina Faso, malaria remains a major issue despite the use of insecticide-treated nets (ITNs), especially in underserved communities. A study assessed a mobile phone-based Interactive Voice Response (IVR) service in local languages to improve malaria health literacy. A randomized trial compared users of the local language version with those using a French version. The results showed a 30.33% increase in knowledge, 21.77% in attitude, and 35.38% in practice among the local language group, while the French group had lower improvements. Key factors for adoption included usability, privacy, trust, and compatibility. Participants preferred local language and voice interfaces, with younger users favoring games and older ones preferring text-based interfaces. This study highlights the potential of local language IVR to effectively boost malaria health literacy and combat misinformation in underserved areas.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0010.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.315
Teacher spread0.291 · 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