An Assessment Framework for e-Mental Health Apps in Canada: Results of a Modified Delphi Process
Notice bibliographique
Résumé
BACKGROUND: The number of e-mental health apps is increasing rapidly. Studies have shown that the use of some apps is beneficial, whereas others are ineffective or do not meet users' privacy expectations. Individuals and organizations that curate, recommend, host, use, or pay for apps have an interest in categorizing apps according to the consensus criteria of usability and effectiveness. Others have previously published recommendations for assessing health-related apps; however, the extent to which these recommendations can be generalized across different population groups (eg, culture, gender, and language) remains unclear. This study describes an attempt by Canadian stakeholders to develop an e-mental health assessment framework that responds to the unique needs of people living in Canada in an evidence-based manner. OBJECTIVE: The objective of our study was to achieve consensus from a broad group of Canadian stakeholders on guiding principles and criteria for a framework to assess e-mental health apps in Canada. METHODS: We developed an initial set of guiding principles and criteria from a rapid review and environmental scan of pre-existing app assessment frameworks. The initial list was refined through a two-round modified Delphi process. Participants (N=25) included app developers and users, health care providers, mental health advocates, people with lived experience of a mental health problem or mental illness, policy makers, and researchers. Consensus on each guideline or criterion was defined a priori as at least 70% agreement. The first round of voting was conducted electronically. Prior to Round 2 voting, in-person presentations from experts and a persona empathy mapping process were used to explore the perspectives of diverse stakeholders. RESULTS: Of all respondents, 68% (17/25) in Round 1 and 100% (13/13) in Round 2 agreed that a framework for evaluating health apps is needed to help Canadian consumers identify high-quality apps. Consensus was reached on 9 guiding principles: evidence based, gender responsive, culturally appropriate, user centered, risk based, internationally aligned, enabling innovation, transparent and fair, and based on ethical norms. In addition, 15 informative and evaluative criteria were defined to assess the effectiveness, functionality, clinical applicability, interoperability, usability, transparency regarding security and privacy, security or privacy standards, supported platforms, targeted users, developers' transparency, funding transparency, price, user desirability, user inclusion, and meaningful inclusion of a diverse range of communities. CONCLUSIONS: Canadian mental health stakeholders reached the consensus on a framework of 9 guiding principles and 15 criteria important in assessing e-mental health apps. What differentiates the Canadian framework from other scales is explicit attention to user inclusion at all stages of the development, gender responsiveness, and cultural appropriateness. Furthermore, an empathy mapping process markedly influenced the development of the framework. This framework may be used to inform future mental health policies and programs.
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 | 0,141 | 0,100 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,021 | 0,006 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,004 | 0,010 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».