Dissection versus Prosection: A Comparative Assessment of the Course Experiences, Approaches to Learning, and Academic Performance of Non‐medical Undergraduate Students in Human Anatomy
Bibliographic record
Abstract
Many institutions rely upon prosection-based laboratories as more resource-efficient and time-effective alternatives to traditional cadaver dissection for human anatomy education. To facilitate growing enrollment numbers despite resource limitations, the University of Guelph (a non-medical institution) introduced a modified "stepwise" prosection-based laboratory cohort to supplement a dissection-based course. In this design, all students attended the same lectures, but those in the dissection-based cohort learned by performing regional dissections and students in the prosection-based cohort studied from those dissections. Prosection students thereby witnessed a "slow reveal" of structures throughout the course. This study compared the perceived course experiences, student approaches to learning, and academic performance between the two groups. Multiple linear regression analyses were used to isolate the effect of the laboratory environment on student approaches to learning and academic performance from demographic and situational covariates. Both groups reported positive course experience ratings and high average final grades that were not statistically dissimilar (P > 0.05), increased reliance on deep approaches to learning (P = 0.002), and decreased reliance on surface approaches to learning (P = 0.023). When controlling for covariates, participation in dissection had small but statistically significant positive associations with deep approaches to learning (P = 0.043), performance on laboratory oral assessments (P < 0.001), and average final grades (P = 0.039). Ultimately, both designs promoted meaningful learning and desirable performance outcomes, indicating that both dissection and stepwise prosection have the potential to facilitate high quality human anatomy instruction.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".