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Record W2806775351 · doi:10.1177/0192513x18777839

Working While Caring for Mom, Dad, and Junior Too: Exploring the Impact of Employees’ Caregiving Situation on Demands, Control, and Perceived Stress

2018· article· en· W2806775351 on OpenAlexaff
Michael Halinski, Linda Duxbury, Christopher D. Higgins

Bibliographic record

VenueJournal of Family Issues · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsWestern UniversityCarleton UniversityToronto Metropolitan University
Fundersnot available
KeywordsStructural equation modelingPsychologyControl (management)Work (physics)BurnoutPerceived controlDual (grammatical number)Work hoursStress (linguistics)Social psychologyDemographic economicsClinical psychologyManagementEconomics

Abstract

fetched live from OpenAlex

Although demographic evidence suggests that, in the developed world, the number of employees who provide both childcare and eldercare is substantial, we know very little about how these “sandwiched” employees differ from those who provide only one form of caregiving (i.e., childcare, eldercare). In this article, we use partial least squares structural equation modeling to examine dual-income employees in households with three different caregiving situations: employees with only childcare ( n = 4,129), only eldercare ( n = 599), and both childcare and eldercare ( n = 767). Findings show that demands contribute to stress more for sandwiched employees than those who provided only one form of caregiving, and more for employees with only eldercare demands than employees with only childcare demands. Results also indicate that control (at work, at home) negatively moderates the relationship between demands (at work, at home) and stress for some employees but not others.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.374

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 studies0.0000.000
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 head0.090
GPT teacher head0.354
Teacher spread0.265 · 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 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

Citations24
Published2018
Admission routes1
Has abstractyes

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