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Record W2885593065 · doi:10.1177/1403494818785042

A scoping review of research to assess the frequency, types, and reasons for end-of-life care setting transitions

2018· review· en· W2885593065 on OpenAlexaff
Donna M. Wilson, Stephen Birch

Bibliographic record

VenueScandinavian Journal of Public Health · 2018
Typereview
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMcMaster UniversityUniversity of Alberta
Fundersnot available
KeywordsGerontologyMedicineEnd-of-life carePsychologyNursingPalliative care

Abstract

fetched live from OpenAlex

Aims: Most people approaching the end of life develop care needs, which typically change over time. Moves between care settings may be required as health deteriorates. However, in some cases, care setting transitions may have little to do with end-of-life care needs and instead reflect the needs, demands, availability, or funding provisions of the country or funding body and organizations providing care. This paper is a scoping review of the international peer-reviewed research literature to gain evidence on the frequency and types of end-of-life care setting transitions, and the reasons for these moves. Methods: All relevant print and open access research articles published in 2000+ were sought using the Directory of Open Access Journals and EBSCO Discovery Host. Results: A total of 39 research articles were identified and reviewed. However, minimal useful evidence was revealed. Most articles focused solely on hospital admissions near death, and some focused on nursing home admissions, with other moves infrequently studied. Conclusions: This review demonstrates the need to quantify and justify end-of-life care setting transitions as it appears dying people are frequently moved, often as death nears. This research is needed to distinguish transitions related to end-of-life care needs and those arising from pressures on or from care providers and others unrelated to the person’s care needs.

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)0.011
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.379
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.581
GPT teacher head0.600
Teacher spread0.018 · 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 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

Citations12
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueScandinavian Journal of Public HealthSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207