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Record W4386761404 · doi:10.1177/01640275231202260

Social Capital and Formal Volunteering Among Family and Unpaid Caregivers of Older Adults

2023· article· en· W4386761404 on OpenAlexaboutno aff
Sol Baik, Jennifer Crittenden, Rachel A. Coleman

Bibliographic record

VenueResearch on Aging · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
Fundersnot available
KeywordsSpouseContext (archaeology)Educational attainmentPsychologyLogistic regressionSocial capitalQuarter (Canadian coin)Formal educationGerontologySociologyMedicineEconomic growthSocial science

Abstract

fetched live from OpenAlex

Using data from 1745 caregivers in the National Study of Caregiving (2017), this study explores the connection between caregiving and formal volunteering by identifying the relationship between social capital and formal volunteering among family and other unpaid caregivers of older adults. In addition, this study examines the representative prevalence of formal volunteering in caregivers. We conducted logistic regression models along with established volunteerism correlates from the prior research literature. Approximately a quarter of caregivers participated in volunteering (25.4%). Being male, having higher educational attainment, being a spouse, living separately from the care recipient, caregiving for multiple care recipients, having a better quality of relationship with the care recipient, having better psychological well-being, receiving more social support, attending religious services, and participating in group activity were positively associated with formal volunteer participation. Findings underscore the role of both human and social capital, including the caregiving context, in formal volunteering among caregivers.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.623

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 head0.035
GPT teacher head0.363
Teacher spread0.329 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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