The Lifecycle of a Clinical Cadaver: A Practice- Based Ethnography
Bibliographic record
Abstract
Phenomenon Cadavers have long played an important and complex role in medical education. While research on cadaver-based simulation has largely focused on exploring student attitudes and reactions or measuring improvements in procedural performance, the ethical, philosophical, and experiential aspects of teaching and learning with cadavers are rarely discussed. In this paper, we shed new light on the fascinating philosophical moves in which people engage each and every time they find themselves face to face with a cadaver. Approach Over a two-year period (2018/19–2019/20), we applied ethnographic methods (137 hours of observation, 24 interviews, and the analysis of 22 documents) to shadow the educational cadaver through the practical stages involved in cadaver-based simulation: 1. cadaver preparation, 2. cadaver-based skill practice with physicians and residents, and 3. interment and memorial services. We used Deleuze and Guattari’s concepts of becoming and acts of creation to trace the ontological “lifecycle” of an educational cadaver as embedded within everyday work practices. Findings We delineated six sub-phases of the lifecycle, through which the cadaver transformed ontologically from person to donor, body, cadaver, educational cadaver, teacher, and loved one/legacy. These shifts involved a network of bureaucratic, technical, educational, and humanistic practices that shaped the way the cadaver was perceived and acted upon at different moments in the lifecycle. By highlighting, at each phase, 1) the ontological transitions of the cadaver, itself, and 2) the practices, events, settings, and people involved in each of these transitions, we explored questions of “being” as it related to the ontological ambiguity of the cadaver: its conceptualization as both person and tool, simultaneously representing life and death. Insights Engaging deeply with the philosophical questions of cadaver-based simulation (CBS) helped us conceptualize the lifecycle as a series of meaningful and purposeful acts of becoming. Following the cadaver from program entry to interment allowed us to contemplate how its ontological ambiguity shapes every aspect of cadaver-based simulation. We found that in discussions of fidelity in medical simulation, beyond both the physical and functional, it is possible to conceive of a third type: ontological . The humanness of the cadaver makes CBS a unique, irreplaceable, and inherently philosophical, practice.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".