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Record W3117516786 · doi:10.1177/0269216320979277

The determinants of the intensity of home-based informal care among cancer patients in receipt of home-based palliative care

2020· article· en· W3117516786 on OpenAlexafffundabout
Jiaoli Cai, Li Zhang, Denise N. Guerriere, Peter C. Coyte

Bibliographic record

VenuePalliative Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of China
KeywordsReceiptMedicinePalliative careMarital statusFamily medicineNursingGerontologyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Understanding the determinants of the intensity of informal care may assist policy makers in the identification of supports for informal caregivers. Little is known about the utilization of informal care throughout the palliative care trajectory. AIM: The purpose of this study was to analyze the intensity and determinants of the use of informal care among cancer patients over the palliative care trajectory. DESIGN: This was a longitudinal, prospective cohort design conducted in Canada. Regression analysis using instrumental variables was applied. SETTING/PARTICIPANTS: From November 2013 to August 2017, a total of 273 caregivers of cancer patients were interviewed biweekly over the course of the care recipient's palliative care trajectory. The outcome was the number of hours of informal care provided by unpaid caregivers, that is, hours of informal care. RESULTS: The number of hours of informal care increased as patients approached death. Home-based nursing care complemented, and hence, increased the provision of informal care. Patients living alone and caregivers who were employed were associated with the provision of fewer hours of informal care. Spousal caregivers provided more hours of informal care. Patient's age, sex, and marital status, and caregiver's age, sex, marital status, and education were associated with the number of hours of informal care. CONCLUSIONS: The intensity of informal care was determined by predisposing, enabling, and needs-based factors. This study provides a reference for the planning and targeting of supports for the provision of informal 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)0.002
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 score0.088
Threshold uncertainty score0.819

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 head0.071
GPT teacher head0.368
Teacher spread0.298 · 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

Citations13
Published2020
Admission routes3
Has abstractyes

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