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Record W2515166005 · doi:10.1186/s12909-016-0724-z

“There’s no billing code for empathy” - Animated comics remind medical students of empathy: a qualitative study

2016· article· en· W2515166005 on OpenAlexaffabout
Pamela Tsao, Catherine Yu

Bibliographic record

VenueBMC Medical Education · 2016
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsEmpathyComicsMedical educationPsychologyQualitative researchCode (set theory)Computer scienceMedicineSocial psychologySociologyProgramming language

Abstract

fetched live from OpenAlex

BACKGROUND: Physician empathy is associated with improved diabetes outcomes. However, empathy declines throughout medical school training. This study seeks to describe how comics on diabetes affect learning processes for empathy in medical students. METHODS: All first- or second-year students at a Canadian medical school were invited to provide written reflections on two comics regarding diabetes and participate in a focus group. Responses were analyzed qualitatively for emergent themes. Students completed the Jefferson Scale of Physician Empathy (JSPE) at baseline, after the comic, and after the focus group. Linear mixed model statistical analyses were performed. RESULTS: Thirteen first-year and 12 second-year students participated. Qualitative analysis revealed four themes: 1) Empathy decline and its barriers; 2) Impact of the comic and focus group on knowledge, attitudes and skills; 3) Role of the comic in the curriculum as a reminder tool of the importance of empathy; 4) Comics as an effective medium. Baseline mean JSPE scores were 116.4 (SD 10.5) and trended up to 117.2 (SD 12.5) and 119.6 (SD 15.2) after viewing the comics and participating in the focus groups, respectively (p = 0.08). CONCLUSIONS: Animated comics on diabetes are novel methods of reminding students about empathy by highlighting the patient perspective.

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)0.005
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.459
Teacher spread0.395 · 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 designQualitative
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

Citations70
Published2016
Admission routes2
Has abstractyes

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