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Record W4238011457 · doi:10.24124/2019/58970

Factors affecting hospice care use among long-term care facility residents in Canada

2019· dissertation· en· W4238011457 on OpenAlexaboutno aff
Beibei Xiong

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsHospice careRuralityMedicineLong-term careGerontologyPalliative careBivariate analysisQuality of life (healthcare)Multivariate analysisResidential careFamily medicinePopulationNursingEnvironmental healthRural area

Abstract

fetched live from OpenAlex

Hospice care can improve quality of life for persons nearing end of life. Little is known about hospice care practices in long-term care facilities (LTCFs) in Canada. This thesis included 185,715 residents in LTCFs in Canada in 2015 and followed their death records to 2016 to examine the characteristics of residents who received hospice care and those who did not but may have benefitted from it. Univariate, bivariate and multivariate analyses were used depending on the variable type. Results show the actual use of hospice care in LTCFs is very low in Canada (i.e. less than 3%). Residents who received hospice care had more severe and complex clinical needs than those who did not. Findings suggest several possible barriers to hospice use in the LTCF population including ageism, rurality, and disease diagnoses. Immediate action is needed to provide improved access to, and utilization of, hospice care in LTCFs in Canada.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.379
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2019
Admission routes1
Has abstractyes

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