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Record W2068275721 · doi:10.1089/10966210260499032

Correlates of Health Status for Family Caregivers in Bereavement

2002· article· en· W2068275721 on OpenAlexaff
Kevin Brazil, Michel Bédard, Kathleen Willison

Bibliographic record

VenueJournal of Palliative Medicine · 2002
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsVictorian Order of NursesLakehead Psychiatric HospitalMcMaster University
Fundersnot available
KeywordsMedicineFamily caregiversMental healthConfidence intervalGerontologyCaregiver burdenMultivariate analysisLogistic regressionPsychiatryDiseaseDementiaInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this retrospective cohort study was to identify aspects of caregiving associated with health status among family caregivers in bereavement. Study participants included 151 family caregivers of terminally ill patients who had died, on average, 294 days prior to the study telephone interview. The interview covered two main areas: patient characteristics and caregiver characteristics. Multivariate linear regressions revealed that as the age of the care recipient (regression coefficient [b] = -0.32; 95% confidence interval [CI] -0.48,-0.15) and caregiver (b = -0.14; 95% CI = -0.25, -0.02) increased, caregivers experienced a decline in their physical health during bereavement. Furthermore, caregivers who reported that caregiving interrupted their usual activities (b = -5.97; 95% CI = -9.79, -2.15) had a decline in physical health during bereavement. A poorer mental health status during bereavement was seen in caregivers who reported poor physical health during caregiving (b = -4.31; 95% CI = -8.17, -0.45); and that they received insufficient family support in caregiving (b = -6.01; 95% CI = -9.75, -2.27). It was also revealed that a home death was associated with higher mental health of the caregiver (b = 3.55; 95% CI = 0.26, 6.84). The practice implications of these findings are discussed in this paper.

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)0.001
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 score0.173
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.396
Teacher spread0.307 · 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

Citations36
Published2002
Admission routes1
Has abstractyes

Explore more

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