Communication skills in psychiatry for undergraduate students: A scoping review
Bibliographic record
Abstract
Communication skills are paramount in all areas of medicine but particularly in psychiatry due to the challenges posed by mental health patients and the essential role of communication from diagnosis to treatment. Despite the prevalence of psychiatric disorders in different medical specialties, particularly in primary care settings, communication skills in psychiatry and their training are not well studied and are often not included in the undergraduate medical curriculum. Our paper explores the relevance of teaching communication competencies in psychiatry for undergraduate medical students. Our work focused on reviewing the methods for teaching communication skills to undergraduate students in Psychiatry. Eleven studies were selected to be included in this review. We found considerable heterogeneity among methods for teaching communication skills but also some common elements such as the use of simulated patients and providing feedback. This review has identified two models: the Calgary-Cambridge interview model and the Kolb cycle-based model. However, most studies still lack a theoretical background model. We believe that the inclusion of communication skills training in medical curricula is fundamental to teaching medical students general communication skills but also specific training on establishing adequate communication with psychiatric patients. However, more research is needed to determine the best method for training but also regarding its translation to patient care and cost-effectiveness.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".