Benefits of extracurricular participation in dissection in a prosection‐based medical anatomy program
Bibliographic record
Abstract
The purpose of this study was to evaluate the extracurricular cadaveric dissection program available to medical students at an institution with a modern (time-compressed, student-centered, and prosection-based) approach to medical anatomy education. Quantitative (Likert-style questions) and qualitative data (thematic analysis of open-ended commentary) were collated from a survey of three medical student cohorts who had completed preclerkship. Perceived benefits of dissection included the hands-on learning style and the development of anatomy expertise, while the main barrier that limited participation was the time-intensive nature of dissection. Despite perceived benefits, students preferred that dissection remain optional. Analysis of assessments for the MD2016 cohort revealed that dissection participation was associated with enhanced performance on anatomy items in each systems-based unit examination, with the largest benefits observed on discriminating items that assessed knowledge application. In conclusion, this study revealed that there are academic and perceived benefits of extracurricular participation in dissection. While millennial medical students recognized these benefits, these students also indicated strong preference for having flexibility and choice in their anatomy education, including the choice to participate in cadaveric dissection. Anat Sci Educ 11: 294-302. © 2017 American Association of Anatomists.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".