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Record W2160915709 · doi:10.1177/0269216309346546

Costs associated with resource utilization during the palliative phase of care: a Canadian perspective

2009· article· en· W2160915709 on OpenAlexafffundabout
Serge Dumont, Philip Jacobs, Konrad Fassbender, Donna Anderson, Véronique Turcotte, François Harel

Bibliographic record

VenuePalliative Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationUniversity of AlbertaUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsPalliative careMedicineFamily medicineNursingHealth careProspective cohort study

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to evaluate prospectively the resource utilization and related costs during the palliative phase of care in five regions across Canada. SUBJECTS: A cohort of 248 patients registered in a palliative care program and their main informal caregivers were consecutively recruited. RESEARCH DESIGN: A prospective research design with repeated measures was adopted. Interviews were conducted at two-week intervals until the patient s passing or up to a maximum of 6 months. MEASURES: The survey questions prompted participants to provide information on the types and number of goods and services they used, and who paid for these goods and services. RESULTS: The largest cost component for study participants was inpatient hospital care stays, followed by home care and informal caregiving time. In regard to cost sharing, the public health care system (PHCS), the family, and not-for-profit organizations (NFPO) sustained respectively 71.3%, 26.6%, and 1.6% of the mean total cost per patient. CONCLUSION: Such results provide a comprehensive picture of costs related to palliative care in Canada, by specifying the cost sharing between the PHCS, the family, and NFPO.

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)0.002
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 score0.278
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.101
GPT teacher head0.416
Teacher spread0.315 · 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

Citations92
Published2009
Admission routes3
Has abstractyes

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