Expanding Youth-Friendly HIV Self-Testing Services During the COVID-19 Pandemic: Qualitative Analysis of a Crowdsourcing Open Call in Nigeria
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,007 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».