Factors Affecting Usability and Acceptability of an Online Platform Used by Caregivers in Child and Adolescent Mental Health Services: Mixed Methods Study
Notice bibliographique
Résumé
Background: Young people and families endure protracted waits for specialist mental health support in the United Kingdom. Staff shortages and limited resources have led many organizations to develop digital platforms to improve access to support. myHealthE is a digital platform used by families referred to Child and Adolescent Mental Health Services in South London. It was initially designed to improve the collection of routine outcome measures and subsequently the "virtual waiting room" module was added, which includes information about child and adolescent mental health as well as signposting to supportive services. However, little is known about the acceptability or use of digital resources, such as myHealthE, or about sociodemographic inequalities affecting access to these resources. Objective: This study aimed to assess the usability and acceptability of myHealthE as well as investigating whether any digital divides existed among its userbase in terms of sociodemographic characteristics. Methods: A survey was sent to all myHealthE users (N=7337) in May 2023. Caregivers were asked about their usage of myHealthE, their levels of comfort with technology and the internet. They completed the System Usability Scale and gave open-ended feedback on their experiences of using myHealthE. Results: A total of 680 caregivers responded, of whom 45% (n=306) were from a Black, Asian, or a minority ethnic background. Most (n=666, 98%) used a mobile phone to access myHealthE, and many had not accessed the platform's full functionality, including the new "virtual waiting room" module. Household income was a significant predictor of caregivers' levels of comfort using technology; caregivers were 13% more likely to be comfortable using technology with each increasing income bracket (adjusted odds ratio 1.13, 95% CI 1.00-1.29). Themes generated from caregivers' feedback highlight strengths of digital innovation as well as ideas for improvement, such as making digital platforms more personalized and tailored toward an individual's needs. Conclusions: Technology can bring many benefits to health care; however, sole reliance on technology may result in many individuals being excluded. To enhance engagement, clinical services must ensure that digital platforms are mobile friendly, personalized, that users are alerted and directed to their full functionality, and that efforts are made to bridge digital divides. Enhancing dissemination practices and improving accessibility to informative resources on the internet is critical to provide fair access to all using Child and Adolescent Mental Health Services.
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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 | 0,013 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».