User Input in the Development of Digital Sexual Health Tools: A Scoping Review and Guidance for Tool Developers
Notice bibliographique
Résumé
BACKGROUND OR CONTEXT: Studies reporting the use of digital tools to promote the prevention and treatment of sexually transmitted and blood borne infections (STBBIs) have proliferated in recent years. Previous reviews highlight variability in the input sought from users in tool development, and its contribution to impact. OBJECTIVE: This scoping review sought to describe approaches to seeking and utilising user input, with the goal of providing guidance for developers. SEARCH STRATEGY: Searches were conducted in MEDLINE, PsycInfo, and the Social Science Citation Index and results screened by two reviewers. The reference lists of included studies and review papers were also checked. INCLUSION CRITERIA: Peer reviewed qualitative and mixed methods studies seeking user input on digital tools promoting the prevention and treatment of STBBIs, from prototyping onwards, published from after 2014 in English, were included. DATA EXTRACTION AND SYNTHESIS: Reported methods and findings were charted in Excel and synthesised using content analysis to provide an overview of methods and domains of user input and utilisation of this input. MAIN RESULTS: A total of 1838 unique titles and abstracts and the full text of 50 publications were screened. Data was charted from 37 eligible studies reporting findings from 34 projects developing digital health tools, including smartphone/tablet applications, websites/web-based applications, chatbots, interactive automated SMS, and purpose-built tools within dating and social media applications. Studies reported on tools developed for use by diverse target populations. The most common domain of input reported was usability (n = 31), while others-namely, satisfaction (n = 27), acceptability (n = 25), formative (n = 24), impact (n = 22), accessibility (n = 17), and engagement (n = 11)-were reported less consistently. User views were sought using qualitative methods such as interviews, focus groups and open-ended survey questions, more often in combination with quantitative measures such as participant-rated measures and engagement analytics. User suggestions for changes were reported in relation to three in four projects studied but incorporation of changes in less than half of projects. DISCUSSION AND CONCLUSIONS: This review demonstrates considerable homogeneity in reported user input in the development of digital health tools. Input from users as co-designers may improve the impact of tools on their intended outcomes. PATIENT AND PUBLIC CONTRIBUTION: This literature review brought together a group of researchers who have sought user input in the development of digital sexual health tools, but, due to resource limitations, did not involve potential users themselves, who are of diverse and disparate groups.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».