MétaCan
Menu
Back to cohort
Record W2580090653 · doi:10.12968/ijpn.2017.23.1.29

Concept analysis of good death in long term care residents

2017· review· en· W2580090653 on OpenAlexaff
Preetha Krishnan

Bibliographic record

VenueInternational Journal of Palliative Nursing · 2017
Typereview
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsWinnipeg Regional Health Authority
Fundersnot available
KeywordsDementiaGerontologyMeaning (existential)Long-term careMedicinePopulationGood deathCause of deathNursing homesPsychologyPalliative careNursingDiseasePsychotherapist

Abstract

fetched live from OpenAlex

The purpose of this concept analysis paper is to delineate the meaning of good death in long term care (LTC) settings and examine its implications for nursing. The Walker and Avant (2011) method was chosen for this analysis. An in depth literature review identifies uses of the concept and determines the defining attributes of the good death. This paper also illustrates case presentations, antecedents, consequences, empirical referents and implications for clinical practice to clarify the concept of 'good death' in this population. In LTC, death is experienced frequently and is considered the ultimate outcome for most admissions. Much of the existing research on end-of-life care has focused on community dwelling cancer patients whose death trajectory is predictable and who may remain cognitively intact until actively dying. In contrast, the LTC population is older and more likely to suffer from dementia and experience chronic illness for long periods prior to death, and they follow a less predictable death trajectory. In this century, death became the province of older people and the assurance of a good death became the responsibility of those caring for them.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.657
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.276
GPT teacher head0.567
Teacher spread0.291 · 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.

Study designObservational
Domainnot available
GenreReview

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

Citations27
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Palliative NursingSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207