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Older Adults Caring for Older Adults: Physically Active Leisure Lifestyles as a Coping Resource for the Health of Caregivers

2002· article· en· W2062789448 on OpenAlexaffvenue
Roger C. Mannell, Alan W. Salmoni, Lynn Martin

Bibliographic record

VenueLoisir et Société / Society and Leisure · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsWestern UniversityUniversity of Waterloo
Fundersnot available
KeywordsWorkloadCoping (psychology)GerontologyPsychologyStructural equation modelingMental healthLeisure timePhysical activityMedicineClinical psychologyPsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

There is growing evidence that caregiving workloads contribute to increased stress and decreased leisure participation. Given that physically active leisure can be an important coping resource for older adults, increases in stress and decreases in leisure may put the health of older caregivers in double jeopardy. A model of the impact of caregiver workload on physically active leisure, role-related stress, and physical and mental health, and the role of physically active leisure in ameliorating the negative effects of workload was developed and tested using structural equation modeling procedures. One hundred and fifty-two primary caregivers of individuals 60 years of age or over who were, themselves, 60 years of age or over, participated in a survey of lifestyle and caregiving experiences. Higher caregiving workloads were significantly linked to higher levels of role-related stress and poorer physical health, which in turn, were linked to lower levels of psychological well-being and higher levels of depression. However, having a physically active leisure lifestyle contributed to better physical health and less role-related stress mitigating to some extent the negative effects of workload. Physically active leisure did not appear to be directly affected by caregiver workload; however, other factors involved in living closer to care receivers appear to have a negative influence on frequency of participation in physically active leisure.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.024
GPT teacher head0.336
Teacher spread0.312 · 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 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

Citations14
Published2002
Admission routes2
Has abstractyes

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