The value of <scp>in‐person</scp> undergraduate dissection in anatomical education in the time of Covid‐19
Bibliographic record
Abstract
While several innovative pedagogical practices have been developed and implemented in anatomy education since the onset of the coronavirus disease 2019 (Covid-19) pandemic, considering the value of in-person undergraduate dissection remains crucial. In this commentary, a human dissection course at the University of Toronto is used as an example to highlight the value of dissection for undergraduate learners in non-professional programs. In-person dissection allows for real life, anatomical variation, and supports the advancement of students' conceptual knowledge of the human body and visual-spatial abilities. Direct involvement with dissection during undergraduate training also provides students with an opportunity to practice and refine non-technical skills, such as communication and collaboration, while simultaneously promoting the development of students' professional identity formation. Further, dissection is a practical, hands-on experience that can provide students with insight into potential career aspirations related to anatomy and the health professions. It is suggested that as institutions veer from traditional pedagogical practices and evaluate how to best move forward post-pandemic, it is imperative that the value of undergraduate dissection is considered among new innovations in the field of anatomy.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".