Delirium as letting go: An ethnographic analysis of hospice care and family moral experience
Bibliographic record
Abstract
BACKGROUND: Delirium is extremely common in dying patients and appears to be a major threat to the family's moral experience of a good death in end-of-life care. AIM: To illustrate one of the ways in which hospice caregivers conceptualize end-of-life delirium and the significance of this conceptualization for the relationships that they form with patients' families in the hospice setting. DESIGN: Ethnography. SETTING/PARTICIPANTS: Ethnographic fieldwork was conducted at a nine-bed, freestanding residential hospice, located in a suburban community of Eastern Canada. Data collection methods included 15 months of participant observation, 28 semi-structured audio-recorded interviews with hospice caregivers, and document analysis. RESULTS: Hospice caregivers draw on a culturally established framework of normal dying to help families come to terms with clinical end-of-life phenomena, including delirium. By offering explanations about delirium as a natural feature of the dying process, hospice caregivers strive to protect for families the integrity of the good death ideal. CONCLUSION: Within hospice culture, there is usefulness to deemphasizing delirium as a pathological neuropsychiatric complication, in favor of acknowledging delirious changes as signs of normal dying. This has implications for how we understand the role of nurses and other caregivers with respect to delirium assessment and care, which to date has focused largely on practices of screening and management.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| 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".