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Record W4295941486 · doi:10.1177/08982643221125258

Mental Health Benefits and Detriments of Caregiving Demands: A Nonlinear Association in the Canadian Longitudinal Study on Aging

2022· article· en· W4295941486 on OpenAlexafffundabout
Alex Bierman, Yeonjung Lee, Margaret J. Penning

Bibliographic record

VenueJournal of Aging and Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of VictoriaUniversity of Calgary
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaChung-Ang UniversityGovernment of Canada
KeywordsMental healthAssociation (psychology)PsychologyDepression (economics)Life satisfactionLongitudinal studyGerontologyClinical psychologyPsychiatryMedicineSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: This study examines whether the association between caregiving demands and mental health is non-linear and also, whether this non-linear association is contingent on the marital status of the caregiver. METHODS: We analyze the data from the Canadian Longitudinal Study on Aging, applying OLS regression and quadratic interaction terms. RESULTS: A lower level of demands is salubriously associated with symptoms of depression and life satisfaction, but this association becomes deleterious at higher levels of demands. Moreover, a connection to a marital partner extends the benefits of caregiving demands and stems the adverse consequences. DISCUSSION: This research shows that acts of caregiving may not themselves be detrimental. Instead, the degree and way in which caregiving relates to mental health may vary by both the extent of the demands of the caregiving role and familial relationships in which caregivers are embedded.

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)0.005
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 score0.466
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 head0.053
GPT teacher head0.369
Teacher spread0.316 · 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

Citations6
Published2022
Admission routes3
Has abstractyes

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