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Record W1983468592 · doi:10.1177/0269216315580742

Delirium as letting go: An ethnographic analysis of hospice care and family moral experience

2015· article· en· W1983468592 on OpenAlexafffundabout
David Wright, Susan Brajtman, Betty Cragg, Mary Ellen Macdonald

Bibliographic record

VenuePalliative Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMcGill UniversityUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsDeliriumHospice careMedicineEthnographyPalliative careNursingIntensive care medicineSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.203
GPT teacher head0.457
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2015
Admission routes3
Has abstractyes

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