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Record W4312844084 · doi:10.2196/38528

Using a Designathon to Develop an HIV Self-Testing Intervention to Improve Linkage to Care Among Youths in Nigeria: Qualitative Approach Based on a Participatory Research Action Framework

2022· article· en· W4312844084 on OpenAlexvenueno aff
Ifeoma Idigbe, Titilola Gbaja‐Biamila, Sarah E. Asuquo, Ucheoma Nwaozuru, Chisom Obiezu‐Umeh, Kadija M. Tahlil, Adesola Zaidat Musa, David Oladele, Bill G. Kapogiannis, Joseph D. Tucker, Juliet Iwelunmor, Oliver Ezechi

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institute of Allergy and Infectious Diseases
KeywordsPsychological interventionParticipatory action researchMedical educationPsychologyCitizen journalismGeneral partnershipMedicineNursingSociologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: UNAIDS (Joint United Nations Programme on HIV and AIDS) and the Nigeria National HIV/AIDS Strategic Framework recommend HIV self-testing and youth-friendly services to enhance HIV testing, linkage to health services, and prevention. However, the voices of youths are seldom incorporated into interventions. We examined qualitative data generated from a series of participatory events in partnership with Nigerian youths focused on enhancing linkage to care. OBJECTIVE: The aim of this study was to assess youth-initiated interventions developed during a designathon to improve linkage to care and sexually transmitted infection services. METHODS: This study conducted a designathon informed by crowdsourcing principles and the participatory research action framework. A designathon is a multistage process including an open call, a sprint event, and follow-up activities. The open call solicited Nigerian youths (14-24 years old) to develop intervention strategies for linkage to care and youth-friendly health services. A total of 79 entries were received; from this, a subset of 13 teams responded to the open call and was invited to participate in a sprint event over 72 hours. Narratives from the open-call proposals were analyzed using grounded theory to identify emergent themes focused on youth-proposed interventions for linkage to care and youth-friendly services. RESULTS: A total of 79 entries (through the web=26; offline=53) were submitted. Women or girls submitted 40 of the 79 (51%) submissions. The average age of participants was 17 (SD 2.7) years, and 64 of 79 (81%) participants had secondary education or less. Two main themes highlighted strategies for enhancing youths' HIV linkage to care: digital interventions and collaboration with youth influencers. A total of 76 participants suggested digital interventions that would facilitate anonymous web-based counseling, text prompt referrals, and related services. In addition, 16 participants noted that collaboration with youth influencers would be useful. This could involve working in partnership with celebrities, gatekeepers, or others who have a large youth audience to enhance the promotion of messages on HIV self-testing and linkage. The facilitators of youths' linkage included health facility restructuring, dedicated space for youths, youth-trained staff, youth-friendly amenities, and subsidized fees. Barriers to HIV linkage to care among youths included a lack of privacy at clinics and concerns about the potential for breaching confidentiality. CONCLUSIONS: Our data suggest specific strategies that may be useful for enhancing HIV linkage to care for Nigerian youths, but further research is needed to assess the feasibility and implementation of these strategies. Designathons are an effective way to generate ideas from youths.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0020.002
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.660
GPT teacher head0.641
Teacher spread0.018 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2022
Admission routes1
Has abstractyes

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