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Record W4366603132 · doi:10.1177/07334648231172353

A Profile of Positive and Negative Caregiving Experiences Among Canadian Older Adults: The Relevance of Demographic and Care Contexts

2023· article· en· W4366603132 on OpenAlexafffundabout
Yeonjung Lee, Alex Bierman

Bibliographic record

VenueJournal of Applied Gerontology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySample (material)GerontologyRelevance (law)Variance (accounting)WarrantClinical psychologyMedicine

Abstract

fetched live from OpenAlex

This study examines the predictors of burdens and benefits of informal caregiving to caregivers by examining how demographic characteristics and care contexts simultaneously predict separate scales of positive and negative caregiving experiences. The Caregiving, Aging, and Financial Experiences study is a national survey which examines a representative sample of 4010 Canadians between the ages of 65 and 85, including 1641 informal caregivers that are the focus of the current analysis. Seemingly unrelated regression analyses show that there are similarities as well as differences in predictors between the two caregiving experiences. More frequent involvement in caregiving is associated with greater negative caregiving experiences but those are not significant predictors for less positive caregiving experiences. This study demonstrates that there are some overlaps of determinants of the two caregiving experiences, and a few of them are distinct. Further studies should warrant to identify additional, unobserved factors explaining variance in positive caregiving experience.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.254
Teacher spread0.247 · 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 designQualitative
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

Citations3
Published2023
Admission routes3
Has abstractyes

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