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Record W4387055373 · doi:10.1080/09540261.2023.2255281

Evaluation of arts and humanities programs in surgery education: a systematic review

2023· review· en· W4387055373 on OpenAlexaff
Diane Jung, Margot Kelly-Hedrick, Erin Brush, Jacob White, Tracy Moniz, Margaret S. Chisolm

Bibliographic record

VenueInternational Review of Psychiatry · 2023
Typereview
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsThe artsEmpathyMedical humanitiesMEDLINEFlourishingPsychologyInclusion (mineral)MedicineMedical educationHumanitiesVisual artsArtPsychotherapistSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

PURPOSE: This systematic review seeks to understand what outcomes have been reported for arts and humanities programs in surgery education. METHODS: Authors searched Medline ALL (Ovid), Embase.com, Web of Science, and Academic Search Ultimate to identify articles on evaluated arts and humanities programs in surgery education. The search identified 1,282 titles and abstracts, of which 55 underwent independent full-text review. The authors identified 10 articles that met inclusion criteria, from which they collected and analysed data. RESULTS: Medical students were the identified learners in most studies (6/10; 60%). Reflective writing was the arts and humanities activity in half of the studies (5/10; 50%); activities based on film, visual art other than film, literature, or social media in the remaining studies (5/10; 50%). Most studies (8/10; 80%) featured a non-controlled, non-randomized design. Authors categorised 5 studies (50%) as Kirkpatrick Level 1, 4 (40%) as Level 2, and 1 (10%) as Level 3. CONCLUSION: Integration of the arts and humanities into surgery education may promote increased levels of learner reflection and empathy, in addition to improved acquisition of surgical skills. More rigorous evaluation of these programs would clarify the impact of arts and humanities programs on surgery learners.

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 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)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.258
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.221
GPT teacher head0.470
Teacher spread0.250 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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