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Record W2050243583 · doi:10.2190/ag.69.3.d

Caregivers—Who Copes How?

2009· article· en· W2050243583 on OpenAlexaff
Neena L. Chappell, Carren Dujela

Bibliographic record

VenueThe International Journal of Aging and Human Development · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCoping (psychology)NeuroticismPsychologySocial supportPersonalityClinical psychologyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Within gerontological caregiving research, there is a major emphasis on stresses and burdens of this role. Yet there has been little attention directed toward the coping strategies that caregivers engage in to cope with this role and the factors that influence their adoption of different coping strategies. This article examines coping strategies and change in coping strategy over a 1-year period. In particular the differential importance of caregiver capacity (such as social support, health, and personality) compared with careload (such as hours of caregiving and need of the care recipient) is examined within a path model. Data came from a purposive sample of caregivers experiencing heavy demands. Overall, problem-focused coping is used more often than emotion-focused coping (either positive or negative) or seeking social support, but caregivers use all types simultaneously. Caregiver capacity, specifically neuroticism, is the strongest predictor of problem-focused coping with those high in neuroticism less likely to use this strategy. High neuroticism also predicts less use overall and negative emotion-focused coping strategies. Few significant predictors emerge of change; those that did were caregiver capacity, not careload variables. The use of all coping strategies, except seeking social support which remained stable, decreased over a 1-year period.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.491

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 studies0.0010.000
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 head0.051
GPT teacher head0.416
Teacher spread0.365 · 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

Citations18
Published2009
Admission routes1
Has abstractyes

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