The Unintentional Effects on Body Donation Programs of a Competency‐Based Curriculum in Postgraduate Medical Education
Bibliographic record
Abstract
As medical programs place increasing importance on competency‐based training and surgical simulations for residents, anatomy laboratories, and body donation programs find themselves in a position of adapting to changing demands. To better assess the demand for “life‐like” cadaveric specimens and evaluate the possible impacts that competency‐based medical education could have upon the body donation program of McGill University, Canada, the authors tracked, over the course of the last 10 years, the number of soft‐embalmed specimens, along with the number of teaching sessions and the residents enrolled in competency‐based programs that are using cadaveric material. The results reveal that the number of soft‐embalmed specimens used within residency training increased from 5 in 2009 to 35 in 2019, representing an increase from 6% of bodies to 36.5% of the total number of body donors embalmed in this institution. Correspondingly, the number of annual teaching sessions for residents increased from 19 in 2012 to 116 in 2019. These increases in teaching are correlated with increasing number of residents enrolled in competency‐based programs over the last 3 years (Pearson r ranging from 0.9705 to 0.9903, and R 2 ranging from 0.9418 to 0.9808). Those results suggest that the new skill‐centered curricula which require residents to perform specific tasks within realistic settings, exhibit a growing demand for “life‐like” cadaveric specimens. Institutions’ body donation programs must, therefore, adapt to those greater need for cadaveric specimens, which presents many challenges, ranging from the logistical to the ethical.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".