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Record W2014184768 · doi:10.5959/eimj.v5i4.178

Student feedback on an inaugural medical humanities module at XUSOM, Aruba

2013· article· en· W2014184768 on OpenAlexaboutno aff
P Ravi Shankar

Bibliographic record

VenueEducation in Medicine Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesComputer scienceLibrary scienceArt

Abstract

fetched live from OpenAlex

Introduction: Medical humanities (MH) are using subjects traditionally known as the humanities in the education of doctors. Xavier University School of Medicine admits students mainly from the United States (US) and Canada to the undergraduate medical (MD) course. In February 2013 a MH module was offered to the first semester (MD1) students using small group, active learning strategies. Objective: The present study was conducted to obtain student feedback on the module and suggestions for further improvement. Method: Feedback was obtained using a questionnaire during the first week of April 2013. Basic demographic information was noted. Respondents were asked to rate their enjoyment and perceived effectiveness of the module and of different learning activities. Their degree of agreement with a set of fifteen statements was also noted. The median scores were compared among different subgroups of respondents using appropriate tests. Result: Twenty-six of the 30 students (86.7%) participated. The median enjoyment and effectiveness scores were 4 (maximum 5). There were no differences according to respondent characteristics. The overall median score was 8 (maximum 10). The module was regarded as fun and engaging, and taught students how to empathize. Students identified most with the session on the medical student. They wanted shorter but more frequent sessions and wanted the facilitator to provide more background about the paintings shown and to mention different solutions/approaches to the problems presented in the role-plays.

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)0.001
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 score0.775
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0330.000

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 head0.030
GPT teacher head0.378
Teacher spread0.348 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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