A Curriculum on Digital Psychiatry for a US-Based Psychiatry Residency Training Program: Pilot Implementation Study
Bibliographic record
Abstract
BACKGROUND: Digital psychiatry, defined as the application of health technologies to the prevention, assessment, and treatment of mental health illnesses, is a growing field. Interest in the clinical use of these technologies continues to grow. However, psychiatric trainees receive limited or no formal education on the topic. OBJECTIVE: This study aims to pilot a curriculum on digital psychiatry for a US-based psychiatry residency training program and examine the change in learner confidence regarding appraisal and clinical recommendation of digital mental health apps. METHODS: Two 60-minute sessions were presented through a web-based platform to postgraduate year 2-4 residents training in psychiatry at a US-based adult psychiatry residency program. Learner confidence was assessed using pre- and postsession surveys. RESULTS: Matched pre- and postsession quizzes showed improved confidence in multiple domains aligning with the course objectives. This included the structured appraisal of digital mental health apps (P=.03), assessment of a patient's digital health literacy (P=.01), formal recommendation of digital health tools (P=.03), and prescription of digital therapeutics to patients (P=.03). Though an improvement from baseline, mean ratings for confidence did not exceed "somewhat comfortable" on any of the above measures. CONCLUSIONS: Our study shows the feasibility of implementing a digital psychiatry curriculum for residents in multiple levels of training. We also identified an opportunity to increase learner confidence in the appraisal and clinical use of digital mental health apps through the use of a formal curriculum.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".