EPA-0722 – The effectiveness of a new approach using movies in the training of medical students.
Bibliographic record
Abstract
According to Torre's approach, dynamic images from movies may help to reflect on empathy-related issues, to develop a compassionate approach to patients and their sufferance, and to enhance awareness about stigmatizing attitudes. Since its development this method has been used by the Psychiatric Institute of the Università del Piemonte Orientale Amedeo Avogadro, Novara, for the education and training of medical students and, broadly speaking, of people involved in helping professions. To describe the preliminary results about the impact of this intervention in a sample of medical students. cinemeducation seminars lasted 6 months, and included 12 meetings. Movies were discussed from a psychological perspective in a group setting and experiences were integrated with the help of the group-leader. Data were collected anonymously via self-report questionnaires from 40 randomly selected participants. Assessment scales (Attitudes Towards Psychiatry, ATP-30; Social Distance Scale, SDS; Interpersonal Reactivity Index, IRI; Rosenberg Self-Esteem Scale, RSES; Toronto Alexithymia Scale, TAS) were administered both before and after the workshops. a significant increase was found in the ATP-30 score (p = .034), and a reduction of the SDS (p = .022) and IRI-PD (p = .010) scores. although these results are preliminary, an improvement in students’ attitudes towards psychiatry was found, together with an increased ability of students to tolerate their own anxiety when experiencing others’ distress. This approach to movies allows developing both cognitive and emotional knowledge, which should be considered particularly important in medical education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".