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Record W3097677298 · doi:10.1016/j.heliyon.2020.e05378

Co-creation of a health education program for improving the uptake of HIV self-testing among men in Rwanda: nominal group technique

2020· article· en· W3097677298 on OpenAlexaff
Tafadzwa Dzinamarira, Augustin Mulindabigwi, Tivani P. Mashamba-Thompson

Bibliographic record

VenueHeliyon · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsHIV Legal Network
FundersInyuvesi Yakwazulu-Natali
KeywordsCurriculumHuman immunodeficiency virus (HIV)MedicineMedical educationTransformational leadershipPsychologyFamily medicinePolitical sciencePublic relationsPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVE: This study sought to collaborate with key stakeholders to reach a consensus regarding the predominant barriers preventing the uptake of HIV testing services (HTS) by men and co-create an acceptable educational program to improve the knowledge of HIV self-testing (HIVST) among men in Rwanda. METHODS: We employed the nominal group technique to identify a consensus regarding the predominant barriers currently impeding the male uptake of HTS. The health education program content was guided by the ranked barriers. We applied Mezirow's Transformational Learning Theory for curriculum development. RESULTS: Eleven key barriers currently impeding the male uptake of HTS were identified in the nominal group process. The stakeholders co-created an interactive, structured curriculum containing information on the health locus of control; HIV etiology, transmission, diagnosis, status disclosure benefits, care and treatment services; and an overview of the HIVST background and test procedure to address multiple barriers. CONCLUSION: Key stakeholders co-created a comprehensive health education program tailored to men, which integrates education about health beliefs, HIV/AIDS and HIVST. Further studies to assess the effectiveness of the program are needed. It is anticipated that the intervention will improve the uptake of HIVST among men in Kigali, Rwanda.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.029
GPT teacher head0.370
Teacher spread0.341 · 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 designOther design
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

Citations25
Published2020
Admission routes1
Has abstractyes

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