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End‐of‐life care in long‐term care settings for older people: a literature review

2006· review· en· W2158380354 on OpenAlexaff
Katherine Froggatt, Donna Wilson, Christopher Justice, Margaret MacAdam, Karen Leibovici, Janice Kinch, Roger E. Thomas, Jae-Young Choi

Bibliographic record

VenueInternational Journal of Older People Nursing · 2006
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLong-term carePsychological interventionNursingScope (computer science)Work (physics)End-of-life careMedicinePopulation ageingService (business)PopulationScale (ratio)GerontologyPsychologyPalliative careBusinessEnvironmental health

Abstract

fetched live from OpenAlex

Most if not all of end-of-life care for people living in long-term care facilities is provided within the facility. This type of care is likely to increase with population ageing. This paper presents a literature review of the published literature on end-of-life care in long-term care facilities such as nursing homes, aged care facilities, residential care homes and continuing care settings, published between 2000 and 2004. A subset of literature that focuses on the development of practice and the identification of interventions to promote the provision of end-of-life care in care homes is examined in detail. Twenty-five papers are identified and these address modes of service delivery, the introduction of 'interventions' that facilitate care for individuals, and the development of tools. This work remains largely descriptive. Small-scale work dominates, reflecting the initial stages of knowledge development in this area of work. Suggestions for ways to expand the scope of the end-of-life care development work in long-term care settings are presented, as it is time to expand the horizons of these initiatives to become more rigorous and responsive to the needs of older people and their families in this care setting.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.744
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.025
GPT teacher head0.431
Teacher spread0.406 · 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 designSystematic review
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

Citations42
Published2006
Admission routes1
Has abstractyes

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