Barriers to and Facilitators of the Implementation of Digital Mental Health Interventions as Perceived by Primary Care Decision Makers: Content Analysis of Structured Open-Ended Survey Data
Notice bibliographique
Résumé
BACKGROUND: Digital mental health represents a way to increase access to evidence-based psychological support. However, the implementation of digital mental health in routine health care practice is limited, with few studies focusing on implementation. Accordingly, there is a need to better understand the barriers to and facilitators of implementing digital mental health. Existing studies have mainly focused on the viewpoints of patients and health professionals. Currently, there are few studies about barriers and facilitators from the perspective of primary care decision makers, that is, the persons responsible for deciding whether a given digital mental health intervention should be implemented in a primary care organization. OBJECTIVE: The objectives were to identify and describe barriers to and facilitators of the implementation of digital mental health as perceived by primary care decision makers, evaluate the relative importance of different barriers and facilitators, and compare barriers and facilitators reported by primary care decision makers who have versus have not implemented digital mental health interventions. METHODS: A web-based self-report survey was conducted with primary care decision makers responsible for the implementation of digital mental health in primary care organizations in Sweden. Answers to 2 open-ended questions about barriers and facilitators were analyzed through summative and deductive content analysis. RESULTS: The survey was completed by 284 primary care decision makers-59 (20.8%) decision makers representing implementers (ie, organizations that offered digital mental health interventions) and 225 (79.2%) respondents representing nonimplementers (ie, organizations that did not offer digital mental health interventions). Overall, 90% (53/59) of the implementers and 98.7% (222/225) of the nonimplementers identified barriers, and 97% (57/59) of the implementers and 93.3% (210/225) of the nonimplementers identified facilitators. Altogether, 29 barriers and 20 facilitators of implementation were identified related to guidelines; patients; health professionals; incentives and resources; capacity for organizational change; and social, political, and legal factors. The most prevalent barriers were related to incentives and resources, whereas the most prevalent facilitators were related to the capacity for organizational change. CONCLUSIONS: A number of barriers and facilitators were identified that could influence the implementation of digital mental health from the perspective of primary care decision makers. Implementers and nonimplementers identified many common barriers and facilitators, but they differ in terms of certain barriers and facilitators. Common and differing barriers and facilitators identified by implementers and nonimplementers may be important to address when planning for the implementation of digital mental health interventions. For instance, financial incentives and disincentives (eg, increased costs) are the most frequently mentioned barrier and facilitator, respectively, by nonimplementers, but not by implementers. One way to facilitate implementation could be to provide more information to nonimplementers about the actual costs related to the implementation of digital mental health.
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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 | 0,000 | 0,000 |
| 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 | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».