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Record W1984164983 · doi:10.1177/0164027506291749

Older Husbands as Caregivers

2006· article· en· W1984164983 on OpenAlexaff
Francine M. Ducharme, Louise Lévesque, Lise Lachance, Marcellin Gangbè, Steven H. Zarit, Jean Vézina, Chantal Caron

Bibliographic record

VenueResearch on Aging · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversité LavalInstitut Universitaire de Gériatrie de MontréalUniversité du Québec à ChicoutimiUniversité de SherbrookeUniversité de Montréal
Fundersnot available
KeywordsStressorPsychologyDistressPsychological interventionPsychological distressClinical psychologyMental healthPsychiatry

Abstract

fetched live from OpenAlex

The purpose of this study, based on Pearlin et al.'s model, was to determine the factors associated with the health of older husband caregivers and with their intention to end home caregiving for their wives. The results showed subjective stressors to be associated with outcomes. Role overload was linked to psychological distress and to lower self-perceived health, whereas role captivity was associated with psychological distress and the intention to end home caregiving. The likelihood of ceasing home caregiving increased when caregivers reported high relational deprivation. A high percentage of the variance in psychological distress was explained (51%). The quality of prior husband-wife relationships, the frequency of disruptive behaviors, family conflicts, and self-efficacy were associated with this variable. Selfefficacy had a mediating effect between subjective stressors and psychological distress, whereas the number of services received had a moderating effect on the intention to end home caregiving among husbands with high role captivity. This study provides avenues for interventions sensitive to male 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 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)0.004
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.046
GPT teacher head0.419
Teacher spread0.373 · 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

Citations30
Published2006
Admission routes1
Has abstractyes

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