A week in the life of Margarite: the importance of situated identities in withdrawing life support
Bibliographic record
Abstract
Medical technologies like mechanical ventilators have introduced complexities to end-of-life decision-making by placing patients in a liminal ‘zone of indistinction’, where their status as living or dying is uncertain. This paper examines a week in the life of Margarite, a 68-year-old woman intubated and ventilated due to pneumonia, to explore how her identity as a living or dying person was negotiated by healthcare professionals and family members. Drawing on the concept of situated identity, this study reveals how perceptions of Margarite’s status shifted over time and across contexts, influenced by medical information, cultural beliefs, and emotional dynamics. Initially viewed as a fighter, legitimising continued life support, Margarite’s status transitioned for some to that of a dying person, prompting calls for withdrawal of care to ensure a dignified death. Others remained uncertain, perceiving her as caught between these states. This case study highlights the fluid and situated nature of identity in end-of-life care and the challenges posed by indecision in the ‘zone of indistinction’. By centring on Margarite’s journey, this paper sheds light on how identity construction influences the decision to continue or withdraw life support.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".