Student feedback on an inaugural medical humanities module at XUSOM, Aruba
Bibliographic record
Abstract
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.Conclusion: The authors have shown it is possible to have a MH module within the shortened curriculum in a Caribbean school.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".