MétaCan
Menu
Back to cohort
Record W1728035199 · doi:10.1007/s40037-015-0205-9

The current landscape of television and movies in medical education

2015· article· en· W1728035199 on OpenAlex
Marcus Law, Wilson Kwong, Farah Friesen, Paula Veinot, Stella Ng

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePerspectives on Medical Education · 2015
Typearticle
Languageen
FieldHealth Professions
TopicFilm in Education and Therapy
Canadian institutionsUniversity of TorontoToronto East General HospitalWomen's College HospitalQueen's UniversityCanada Research ChairsSt. Michael's Hospital
Fundersnot available
KeywordsNarrativeHumanismMultimediaComputer sciencePsychologyEngineering ethicsMedical educationMedicinePolitical scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Using commercially available television and movies is a potentially effective tool to foster humanistic, compassionate and person-centred orientations in medical students. AIM: We reviewed pedagogical applications of television and movies in medical education to explore whether and why this innovation holds promise. METHODS: We performed a literature review to provide a narrative summary on this topic. RESULTS: Further studies are needed with richer descriptions of innovations and more rigorous research designs. CONCLUSION: As we move toward evidence-informed education, we need an evidence- based examination of this topic that will move it beyond a 'show and tell' discussion toward meaningful implementation and evaluation. Further exploration regarding the theoretical basis for using television and movies in medical education will help substantiate continued efforts to use these media as teaching tools.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.485
Teacher spread0.448 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it