Dying on television versus dying in intensive care units following withdrawal of life support: how normative frames may traumatise the bereaved
Bibliographic record
Abstract
While treatment is often withdrawn from patients in intensive care units (ICUs), few people outside the healthcare profession have witnessed a death under such circumstances. Family members who have made the decision to withdraw treatment may have expectations about the dying process, what constitutes a good death and how they should behave in an ICU based on popular prime-time television series. An inductive comparative thematic coding strategy is therefore used to examine how death following treatment withdrawal as depicted in a US medical drama (Grey's Anatomy) differs from realities observed for 6 months fieldwork at an ICU in Canada. Three common frames (privacy, emotional control and memorialising) help patients' intimates normalise the unfamiliar experience and guide their behaviour during the event. However, discrepancies between media representations and experiences in the ICU, especially around the frames of timing of death and the physicality of the unbounded body (incontinence and agonal breathing), can traumatise them. The bereaved may be left viewing ventilator withdrawal and dying as chaotic processes and believing their loved one suffered through a bad death. Understanding these normative and discrepant frames should help healthcare professionals better prepare the public to witness death.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".