Death and dignity through fresh eyes
Bibliographic record
Abstract
BACKGROUND: Trinity College Dublin remains one of the Medical Schools that uses traditional dissection to teach anatomy, exposing students from the first week of entry to cadavers. This early exposure makes it imperative that issues surrounding death and donor remains are explored early on within the main structure of the curriculum. CONTEXT: The School of Medicine began a programme of Medical Humanities student-selected modules (SSMs) in 2010, and the opportunity to offer a module on medical ethics regarding death and dignity was taken. INNOVATION: A course was devised that touched only lightly on subjects such as palliative care and the concept of a good death. The course focused much more strongly on the reality of death as part of cultural and societal identity and placement. This was facilitated by field trips to settings where discussions regarding death, dying and dignity were commonplace and authentic experiences, rather than classroom discussions based on theoretical circumstances that may not yet have been experienced by the student. IMPLICATIONS: The module ran very well, with students feeling that they had had a chance to think critically about the role of death as an event with significance within society and culture, rather than purely in a medico-legal framework. Options to extend the module to the compulsory element of the course, to be built upon in later years looking at more technical aspects surrounding death, are being explored.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.083 | 0.008 |
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".