Dissemination Strategies for HIV Self-testing Intervention Among Adolescents and Young Adults in Nigeria: Findings from Crowdsourcing Open Call (Preprint)
Bibliographic record
Abstract
BACKGROUND: Despite many evidence-based HIV interventions, most research findings are not disseminated. Enhancing research dissemination is essential to achieving Nigeria's HIV targets. OBJECTIVE: This study describes dissemination strategies solicited from the public to share research findings and products from an HIV self-testing intervention among Nigerian adolescents and young adults (AYAs). METHODS: A crowdsourcing open call was launched in 2023 for 3 weeks to generate creative ideas for strategies to disseminate research findings and products from an HIV self-testing intervention among young people (aged 14-30 years). Crowdsourcing open calls are structured approaches to solicit community insights. Two people prescreened the submissions for eligibility before judging based on 5 criteria: clear description, desirability to AYAs, innovation, feasibility, and relevance to dissemination. We then conducted an inductive thematic content analysis of eligible submissions to identify key dissemination strategies for HIV research targeting AYAs and used descriptive statistics to characterize participants' demographic data. RESULTS: We received 64 eligible submissions from 24 states in Nigeria. Most (n=34, 53%) participants were male, and (n=18, 28%) were aged ≤18 years (mean age 21, SD 3.08 years). Our analysis identified six major themes reflecting AYA-driven strategies for disseminating HIV research products: (1) the use of digital platforms such as social media, websites, podcasts, and blogs; (2) the use of artistic expressions such as murals, posters, comics, and infographics to communicate key research findings in visually appealing ways tailored to AYAs; (3) community engagement and experiential activities such as door-to-door campaigns and interactive workshops to communicate research products and findings; (4) performance arts such as the use of concerts, open-mic events, dance performances, and fashion events to communicate research findings; (5) storytelling and narratives in the form of stories, drama, and theater as tools for dissemination; and (6) interactive and gamified activities such as mobile apps and board games to engage AYAs with research findings and products. Most submissions (39/64, 61%) recommended local collaboration with schools or community leaders for dissemination. CONCLUSIONS: The crowdsourcing open call was feasible and effective in generating ideas for research dissemination among AYAs. Our findings have implications for enhancing research dissemination among AYA populations in several resource-limited settings. Future work should involve evaluating the reach, acceptability, cost, and impact of these dissemination strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".