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Record W4297340746 · doi:10.1159/000526251

Examination of the Healthy Caregiver Effect among Older Adults: Findings from the Canadian Longitudinal Study on Aging

2022· article· en· W4297340746 on OpenAlexafffundabout
Lun Li, Andrew Wister, Barbara Mitchell

Bibliographic record

VenueGerontology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsMacEwan UniversitySimon Fraser University
FundersCanadian Institutes of Health Research
KeywordsGerontologyHealthy agingMedicineLongitudinal studyHealth and Retirement StudyHealthy ageingAgeingPsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: The Healthy Caregiver Hypothesis (HCH) suggests that caregiving is associated with beneficial health impacts for family caregivers. However, mixed results have been reported, particularly when different levels of caregiving intensity were examined. This study analyzes the relationship between caregiving intensity and three health indicators (functional health, chronic illness, and self-rated general health) among Canadian older adults over 3 years. METHODS: We drew upon a subsample of 11,344 participants aged 65 years and older from the Baseline and Follow-up 1 data of the Canadian Longitudinal Study on Aging and used linear mixed models to test the hypothesis based on different levels of caregiving intensity. RESULTS: Older adults who provided low-intensity care recently or continuously reported better functional health and self-rated health than noncaregivers. In contrast, older adults with low-intensity caregiving responsibility reported more chronic conditions over time compared to noncaregivers, but this association was not found for high-intensity caregivers. DISCUSSION/CONCLUSION: This study elucidates the HCH by incorporating caregiving intensity to understand patterns of better functional health and perceived health but more chronic conditions. The findings yielded from different health indicators suggest the impact of caregiving on health may be domain specific.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.025
GPT teacher head0.302
Teacher spread0.277 · 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.

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

Citations11
Published2022
Admission routes3
Has abstractyes

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