Preferences Regarding Information Strategies for Digital Mental Health Interventions Among Medical Students: Discrete Choice Experiment
Notice bibliographique
Résumé
BACKGROUND: Digital mental health interventions (DMHIs) are capable of closing gaps in the prevention and therapy of common mental disorders. Despite their proven effectiveness and approval for prescription, use rates remain low. The reasons include a lack of familiarity and knowledge as well as lasting concerns. Medical students were shown to have a comparatively higher risk for common mental disorders and are thus an important target group for raising awareness about DMHIs. At best, knowledge is already imparted during medical school using context-sensitive information strategies. Yet, little is known about medical students' information preferences regarding DMHIs. OBJECTIVE: This study aims to explore information preferences for DMHIs for personal use among medical students in Germany. METHODS: A discrete choice experiment was conducted, which was developed using an exploratory sequential mixed methods research approach. In total, 5 attributes (ie, source, delivery mode, timing, recommendation, and quality criteria), each with 3 to 4 levels, were identified using formative research. Data were analyzed using logistic regression models to estimate preference weights and the relative importance of attributes. To identify subgroups of students varying in information preferences, we additionally performed a latent class analysis. RESULTS: Of 309 participants, 231 (74.8%) with reliable data were included in the main analysis (women: 217/309, 70.2%; age: mean 24.1, SD 4.0 y). Overall, the conditional logit model revealed that medical students preferred to receive information about DMHIs from the student council and favored being informed via social media early (ie, during their preclinic phase or their freshman week). Recommendations from other students or health professionals were preferred over recommendations from other users or no recommendations at all. Information about the scientific evidence base was the preferred quality criterion. Overall, the timing of information was the most relevant attribute (32.6%). Latent class analysis revealed 2 distinct subgroups. Class 1 preferred to receive extensive information about DMHIs in a seminar, while class 2 wanted to be informed digitally (via email or social media) and as early as possible in their studies. CONCLUSIONS: Medical students reported specific needs and preferences regarding DMHI information provided in medical school. Overall, the timing of information (early in medical education) was considered more important than the information source or delivery mode, which should be prioritized by decision makers (eg, members of faculties of medicine, universities, and ministries of education). Study findings suggest general and subgroup-specific information strategies, which could be implemented in a stepped approach. Easily accessible digital information may promote students' interest in DMHIs in the first step that might lead to further information-seeking behavior and the attendance of seminars about DMHIs in the second step.
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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 | 0,010 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 ».