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Record W1981357916 · doi:10.1093/geront/gnv038

Caregiver Stress and Mental Health: Impact of Caregiving Relationship and Gender

2015· article· en· W1981357916 on OpenAlexaffabout
Margaret J. Penning, Zheng Wu

Bibliographic record

VenueThe Gerontologist · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSpouseMental healthPsychologyAffect (linguistics)Clinical psychologySocial supportFamily caregiversGerontologyPsychiatryMedicineSocial psychology

Abstract

fetched live from OpenAlex

PURPOSE OF THE STUDY: This study compared the stress and mental health implications of caregiving to a spouse, children, siblings, other family members, friends, and others among middle-aged and older male and female caregivers. DESIGN AND METHODS: Multivariate regression analyses were conducted using 2007 Canadian General Social Survey data collected on a subsample of caregivers aged 45 and older. RESULTS: Our analyses revealed that for women, caring for a spouse or children was more stressful and detrimental to mental health than caring for parents or others. Similarly, for men, caring for a spouse and for children was more stressful than caring for others but did not adversely affect overall mental health. IMPLICATIONS: The findings suggest that spousal and child caregiving tend to be more rather than less stressful and detrimental to middle-aged and older caregivers' mental health than is caregiving to most others but that gender differences need to be considered.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.096
GPT teacher head0.377
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), 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

Citations406
Published2015
Admission routes2
Has abstractyes

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