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Record W2050013685 · doi:10.12968/ijpn.2008.14.7.30772

Staff opinions about the components of a good death in long-term care

2008· article· en· W2050013685 on OpenAlexaffabout
Maggie Gibson, Iris Gutmanis, Heather Clarke, Deb Wiltshire, Andrew Feron, Eunice Gorman

Bibliographic record

VenueInternational Journal of Palliative Nursing · 2008
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsThe King's UniversityWestern UniversityParkwood Institute
Fundersnot available
KeywordsContext (archaeology)PopulationPsychologyMedicinePalliative careFear of deathScale (ratio)Consistency (knowledge bases)GerontologyNursingPsychiatryEnvironmental healthHistory

Abstract

fetched live from OpenAlex

AIM: The aim of this study was to identify differences of opinion about the relative importance of different components of a good death among care providers in a long-term care home serving Canadian war veterans. METHODS: The Concept of a Good Death scale (Swartz et al, 2003), with slight adaptations to fit the long-term care context, was made available to all staff. Responses were accepted for a one-month period. FINDINGS: Survey return rate was 30.4%. There was a majority (greater than 50%) opinion that 12 of the 20 items were essential or important to a good death, and that three items were not necessary: 'that death is sudden and unexpected' (64.5%), 'that there be control of bodily functions to the end' (61.8%) and 'that there be mental alertness to the end' (55.3%). There was not a majority opinion on the five remaining items: 'that the dying period be short', 'that death occurs naturally without technical equipment', 'that the person lived until a key event', 'that the ability to communicate be present until death', and 'that death occurs during sleep'. CONCLUSIONS: Detailed analysis of survey results identified differences of opinion that could have implications for consistency and quality of care. The findings suggest ways in which the unique characteristics of the long-term care environment and population influence opinions about the components of a good death.

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)0.000
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 score0.021
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.157
GPT teacher head0.460
Teacher spread0.302 · 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

Citations14
Published2008
Admission routes2
Has abstractyes

Explore more

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