Bibliographic record
Abstract
The psychiatric interview serves as the cornerstone of psychiatric practice. It is therefore essential that we find effective ways of teaching students how to conduct a psychiatric interview. The present paper arises from two faculty members at Memorial University of Newfoundland and Labrador considering how to improve the quality of teaching of the psychiatric interview to preclerkship undergraduate medical students, before they begin the clinical portion of their training. The interview is taught in discrete pieces initially (e.g., discussing confidentiality, screening for suicidal ideation, taking a history for depressive disorders, etc.) before being assembled into a whole interview.The sessions are led by psychiatrists and residents who play the role of the patient. They use prewritten cases but can improvise to challenge or direct the students. Students receive real time feedback. The flexibility allows for students to repeat and vary their approach in response to feedback.Anonymous course evaluations showed improvement in student satisfaction with the new psychiatry clinical skills teaching. Prior to implementing the new approach student satisfaction was at 3.9/5. With the new method scores improved to 4.7/5 and 4.5/5 in the following two years. Clinical skills OSCE scores remained stable with modest improvement following implementation. The class average was 8.5 in the year prior to implementation and were 9.1, 8.6 and 8.8 in the years following. As a side benefit, the approach lent itself well to being delivered remotely so it continued to function well during the disruption resulting from COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".