The effectiveness of a new approach using movies in the training of medical students
Bibliographic record
Abstract
INTRODUCTION: The use of movies in medical (particularly psychiatric) education has been often limited to portraits of mental illness and psychiatrists. The Psychiatric Institute of the Università del Piemonte Orientale has a longstanding tradition of working with/on movies according to a method developed by Eugenio Torre, using dynamic images as educational incitements. Our aim is to describe the preliminary results on the impact of this intervention in medical students. METHODS: The cinemeducation project lasted 6 months, and included 12 meetings. Forty randomly selected participants were assessed with: Attitudes Towards Psychiatry Scale (ATP-30), Social Distance Scale (SDS), Interpersonal Reactivity Index (IRI), and Toronto Alexithymia Scale (TAS), both at baseline and after 6 months, when the workshop was concluded. RESULTS: A significant increase was found in the ATP-30 score, and a reduction of the SDS and IRI-Personal Distress scale scores. CONCLUSIONS: Informal feedback from participants was strongly positive. Preliminary results from the assessment of participants are encouraging. Students' attitudes towards psychiatry and ability to tolerate anxiety when experiencing others' distress improved, while stigma decreased. The evocative power of movie dynamic images, developed in the group and integrated with the help of the group leader, can enrich students' knowledge, both from a cognitive and emotional standpoint.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".