Les représentations médiatiques de la mort cérébrale : \nperspectives publiques, débats d’experts et enjeux éthiques
Bibliographic record
Abstract
Despite being widely accepted by the medical community, the concept of brain death is still misunderstood by healthcare providers and the general public.It is central to several debates and remains a source of controversy.In spite of documented practice variations and conceptual confusion surrounding brain death, the upstream sources of variability and uncertainty have not been extensively investigated.This confusion is likely to influence debates and decisions about end-of-life and organ donation, and thus raises serious medical and ethical considerations.This thesis first proposes a review of the literature discussing the origins, the evolution and the debates related to the concept of brain death, as well as the ethical issues associated with brain death.The methodological approaches used for the realization of this research are then described.The results obtained from the qualitative content analysis of Canadian and American media about the different depictions of brain death follow.These results describe the use of the term "brain death" in contexts of organ donation, definitions of brain death, determination of death, end-of-life issues, legal issues and also colloquial uses of the term.Finally, a general discussion about the role of the terminology used to discuss the concept and that of healthcare professionals involved in its determination and organ donation practices, as well as recommendations for the future will conclude this thesis.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.002 |
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".