Cinemeducation: Facilitating educational sessions for medical students using the power of movies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medical education focuses predominantly on the science of medicine neglecting the arts and human relationships. Medical humanities was developed to provide a “differing” perspective of the arts. Movies play an important role in the medical humanities and have been used to address various subjects such as medical ethics, doctor–patient relationship, clinical research, mental illness, and professionalism during medical school. Movies involve the affective domain, promote reflection, and link learning to experiences. Movies can teach empathetic behaviors, self-reflection, compassion, and other skills. Movies have been used in a variety of disciplines such as family medicine, psychiatry, internal medicine, and clinical pharmacology among others. Faculty should identify possible topics where movies can be used. Then, they have to create a shortlist of suitable movies and identify the movie to be screened. A list of suitable activities and exercises to promote critical analysis and reflection should be created. Before the screening, a brief introduction to the movie can be provided. The screening should be followed by group activities, presentations, and facilitator inputs. Movies have been used to address topics such as domestic violence, cultural medicine, and attitude toward chronic illness. Most published reports about the use of movies are from the USA. Reports from Canada, Europe, and Argentina are also common. Movies have been used in some Caribbean medical schools and are being increasingly used in South Asian medical schools. A variety of instruments can be used to obtain feedback. There are various databases and collections which will be helpful in choosing appropriate movies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it