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Record W3080171719 · doi:10.1080/0142159x.2020.1807482

Patients as teachers and arts-based reflection in surgical clerkship: A preliminary exploration

2020· article· en· W3080171719 on OpenAlexafffundabout
Emilia Kangasjarvi, Stella Ng, Farah Friesen, Jory Simpson

Bibliographic record

VenueMedical Teacher · 2020
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsWomen's College HospitalUniversity of TorontoSt. Michael's Hospital
FundersDepartment of Family and Community Medicine, University of TorontoUniversity of Toronto
KeywordsCurriculumMedical educationHumanismThe artsMedicinePsychologyReflection (computer programming)Focus groupPedagogySociology

Abstract

fetched live from OpenAlex

BACKGROUND: Involving patients in medical education as teachers is not a novel approach, yet it has not been widely adopted by undergraduate surgical curricula in Canada. The Patients as Teachers initiative in surgery (PAT) program, with an arts-based reflection assignment, was developed for surgical clerks with the goals of emphasizing patient-centredness in surgical practice, humanistic aspects of medicine, and to counterbalance the commonplace emphasis on technical competency in surgery. METHODS: = 46) were invited to participate in focus groups at the end of the program. RESULTS: Findings converged around two main themes: what students/patient teachers valued about the PAT program and what they perceived was learned. While patient teachers felt a sense of emotional healing and appreciated a chance to contribute to medical education, students valued having protected time to learn in depth from the patient teachers. Students also begrudgingly came to appreciate the arts-based reflection assignment. CONCLUSION: By bringing patient voice to the forefront and encouraging reflection, the PAT program emphasized to students the compassionate and humanistic side of surgical care. Future studies could examine the mechanisms by which learning occurs and long-term impacts.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.070
GPT teacher head0.371
Teacher spread0.302 · 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.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations13
Published2020
Admission routes3
Has abstractyes

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