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Record W2411841099 · doi:10.1177/082585971002600403

How End-Of-Life Home Care Services Are Used from Admission to Death: A Population-Based Cohort Study

2010· article· en· W2411841099 on OpenAlexafffundabout
Hsien Seow, Lisa Barbera, Doris Howell, Sydney M. Dy

Bibliographic record

VenueJournal of Palliative Care · 2010
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoMcMaster UniversityJuravinski Cancer Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineEnd-of-life careOdds ratioCohort studyOddsConfoundingCohortPopulationNursing homesProspective cohort studyNursingGerontologyPalliative careEmergency medicineEnvironmental healthLogistic regressionInternal medicine

Abstract

fetched live from OpenAlex

AIM: Our goal was to describe the trajectories of end-of-life nursing and personal support worker (PSW) use from home care admission until death. METHODS: We studied a historical prospective cohort of end-of-life home care patients in Ontario, Canada, linking administrative databases. We calculated the odds of using any nursing or PSW hours and the incidence rate ratio of services used for each week approaching death, controlling for confounders. RESULTS: Among all patients (n = 11,867), the odds of using any nursing and PSW hours increased by 4 percent and 10 percent, respectively, each week closer to death. Among patients using services, the ratio of nursing and PSW hours increased 20 percent and 11 percent, respectively, in the last 4 weeks of life compared to use at 24 weeks before death. CONCLUSION: Use of nursing and PSW hours increases slightly each week before death and sharply in the last month of life. Understanding the trajectory of home care services use can help decision makers design better end-of-life care.

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 score0.152
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.054
GPT teacher head0.403
Teacher spread0.349 · 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

Citations15
Published2010
Admission routes3
Has abstractyes

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