Expanding Youth-Friendly HIV Self-Testing Services During the COVID-19 Pandemic: Qualitative Analysis of a Crowdsourcing Open Call in Nigeria
Bibliographic record
Abstract
BACKGROUND: HIV self-testing (HIVST) among young people is an effective approach to enhance the uptake of HIV testing recommended by the World Health Organization. However, the COVID-19 pandemic disrupted conventional facility-based HIV testing services, necessitating the exploration of innovative strategies for the effective delivery of HIVST. OBJECTIVE: This study analyzed the outcomes of a digital World AIDS Day crowdsourcing open call, designed to elicit youth responses on innovative approaches to promote HIVST among young people (14-24 years) in Nigeria during COVID-19 restrictions. METHODS: From November 2 to 22, 2020, a World AIDS Day 2020 crowdsourcing open call was held digitally due to COVID-19 restrictions. The crowdsourcing open call followed World Health Organization standardized steps, providing a structured framework for participant engagement. Young people in Nigeria, aged 10-24 years, participated by submitting ideas digitally through Google Forms or email in response to this crowdsourcing open call prompt: "How will you promote HIV self-testing among young people during COVID-19 pandemic?" Data and responses from each submission were analyzed, and proposed ideas were closely examined to identify common themes. Four independent reviewers (AE, SM, AZM, and TG) judged each submission based on the desirability, feasibility, and impact on a 9-point scale (3-9, with 3 being the lowest and 9 being the highest). RESULTS: The crowdsourcing open call received 125 eligible entries, 44 from women and 65 from men. The median age of participants was 20 (IQR 24-20) years, with the majority having completed their highest level of education at the senior secondary school level. The majority of participants lived in the South-West region (n=61) and Lagos state (n=36). Of the 125 eligible entries, the top 20 submissions received an average total score of 7.5 (SD 2.73) or above. The panel of judges ultimately selected 3 finalists to receive a monetary award. Three prominent themes were identified from the 125 crowdsourcing open call submissions as specific ways that HIVST can adapt during the COVID-19 pandemic: (1) digital approaches (such as gamification, photoverification system, and digital media) to generate demand for HIVST and avoid risks associated with attending clinics, (2) awareness and sensitization through existing infrastructures (such as churches, schools, and health facilities), and (3) partnerships with influencers, role models, and leaders (such as religious and youth leaders and social influencers in businesses, churches, organizations, and schools) to build trust in HIVST services. CONCLUSIONS: The crowdsourcing open call effectively engaged a diverse number of young people who proposed a variety of ways to improve the uptake of HIVST during the COVID-19 pandemic. Findings contribute to the need for innovative HIVST strategies that close critical knowledge and practice gaps on ways to reach young people with HIVST during and beyond the pandemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT04710784; https://clinicaltrials.gov/study/NCT04710784.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".