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Record W4413818070 · doi:10.2196/65116

Developing a Health Education Program for the Prevention and Control of Infectious Diseases Culturally Adapted to Ethnic and Rural Communities: Co-Design Study Using Participatory Audiovisual Methods

2025· article· en· W4413818070 on OpenAlexvenueno aff
Martha Milena Bautista-Gómez, Laura Sofía Zuluaga

Bibliographic record

VenueJournal of Participatory Medicine · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
FundersSistema General de Regalías de Colombia
KeywordsEthnic groupQualitative researchSanitationQualitative propertyMedical educationHealth educationMedicinePsychologyPublic healthNursingSociologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Infectious diseases disproportionately affect rural and ethnic communities in Colombia, where structural inequalities such as limited access to health care, poor sanitation, and scarce health education worsen their effects. Education is essential for preventing and controlling infectious diseases, fostering awareness of healthy behaviors, and empowering communities with the knowledge and skills to manage their health. Participatory and co-design methods strengthen educational programs by ensuring cultural relevance, enhancing knowledge retention, and promoting sustainable community interventions. OBJECTIVE: This study aims to describe the co-design process and evaluate the capacity building of an education program for the prevention and control of infectious diseases using participatory audiovisual methods culturally adapted to ethnic communities and rural contexts in Colombia. METHODS: A qualitative case study approach was used. 15 community leaders contributed to the program's design, implementation, and evaluation. Nominal groups and a participatory social diagnosis identified key topics, while theoretical-practical sessions with visual methods guided the cocreation of workshops and audiovisual materials. Evaluation combined qualitative analysis of participants' perceptions and quantitative assessment of knowledge acquisition. Qualitative data were coded through content analysis, while multiple-choice questionnaires (initial and final) categorized knowledge acquisition into 3 levels (low, medium, and high), with percentage distributions used for comparative analysis. RESULTS: The co-design process resulted in 12 theoretical and practical workshops in infectious diseases and 3 audiovisual products: an animation about malaria, a comic book about cutaneous leishmaniasis, and a puppet show about tuberculosis. The quantitative evaluation applied to the 15 participants revealed substantial improvements, with the proportion that achieved excellent scores in pedagogy increasing from 40% (6/15) to 93% (14/15), in leadership from 13% (2/15) to 27% (4/15). In terms of health knowledge, excellent scores increased from 40% for leishmaniasis, 60% for malaria, and 13% for tuberculosis, reaching 80% for all three diseases. The qualitative evaluation showed positive results in terms of the participants' perceptions of both the methodology and the co-design process outcomes. CONCLUSIONS: The co-design process was driven by 3 key factors: (1) active community participation at every stage; (2) knowledge exchange between multidisciplinary technical expertise and practical local knowledge; and (3) the use of innovative, culturally adapted pedagogical tools tailored to the rural context and population. This co-design process proved to be an effective method for meaningful capacity building among populations experiencing vulnerability in complex settings, and has the potential to contribute significantly to the improvement of infectious disease prevention and control.

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)0.020
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.701
GPT teacher head0.718
Teacher spread0.016 · 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 designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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