Uptake of a self‐guided digital treatment for depression and anxiety: A qualitative study exploring patient perspectives and decision‐making
Notice bibliographique
Résumé
BACKGROUND: Despite the demonstrated efficacy and potential scalability of self-guided digital treatments for common mental health conditions, there is substantial variability in their uptake and engagement. This study explored the decision-making processes, influences and support needs of people taking up a self-guided digital treatment for anxiety and/or depression. METHODS: Australian-based adults (n = 20) were purposively sampled from a trial of self-guided digital mental health treatment. One-to-one, semistructured interviews were conducted, based on the Ottawa Decision-Support Framework. Interviews were transcribed verbatim and analysed thematically using framework methods. Baseline sociodemographic, clinical and decision-making characteristics were also collected. RESULTS: Analyses yielded four themes. Theme 1 captured participants' openness to try self-guided digital treatment, despite limited deliberation on potential downsides or alternative options. Theme 2 highlighted that immediacy and ease of access were major drivers of uptake, which participants contrasted with gaps in access and continuity of care in face-to-face services, especially rurally. Theme 3 centred on participants as the main agents in their decision-making, with family and health professional attitudes also reportedly influencing decision-making. Theme 4 revealed participants' primary motivations for deciding to take up treatment (e.g., the potential to increase insight and coping skills), while also acknowledging that pre-existing characteristics (e.g., health and digital literacy, insight) determined participants' personal suitability for self-guided digital treatment. CONCLUSION: Findings help to elucidate the decision-making influences and processes amongst people who started a self-guided treatment for depression and anxiety. Additional information and decision support resources appear warranted, which may also improve the accessibility of self-guided treatments. PUBLIC OR PATIENT CONTRIBUTION: Patients were interviewed about their views and experiences of decision-making about accessing and taking up treatment. As such, patient contribution to the research was as study participants.
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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) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».