Preferences Regarding Information Strategies for Digital Mental Health Interventions Among Medical Students: Discrete Choice Experiment
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".