Best Practices for Teaching Psychotherapy to Medical Students: A Scoping Review
Bibliographic record
Abstract
Psychotherapy is an essential component of mental healthcare, yet its formal instruction within medical curricula remains underdeveloped. This scoping review aimed to map the best practices for teaching psychotherapy to medical students by examining the types of psychotherapy covered and the teaching strategies employed. A systematic search was conducted across the PubMed, Embase, PsycINFO and Google Scholar databases without time restrictions, and studies were selected if they focused on psychotherapy education for medical students. Fifteen studies met the inclusion criteria. The findings revealed that multimodal approaches, combining didactic sessions, experiential learning, clinical exposure and digital content, were the most commonly used and pedagogically effective strategies. Role play and clinical exposition were particularly valued for enhancing communication skills, empathy and therapeutic understanding, while e-learning emerged as a flexible but less frequently used tool. Motivational interviewing was the most frequently taught psychotherapeutic modality, followed by mindfulness, cognitive-behavioral therapy and psychodynamic approaches. Although the overall quality of studies was moderate to high, the heterogeneity in study design and outcome measures limited direct comparisons. These results highlight the need for standardized, experiential and integrated teaching strategies to better prepare future physicians for incorporating psychotherapy principles into clinical practice.
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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 | 0.019 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".