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Record W4229007779 · doi:10.1177/08982643221092876

Something’s Gotta Give: The Relationship Between Time in Eldercare, Time in Childcare, and Employee Wellbeing

2022· article· en· W4229007779 on OpenAlexaff
Linda Duxbury, Michael Halinski, Maggie Stevenson

Bibliographic record

VenueJournal of Aging and Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsToronto Metropolitan UniversityCarleton University
Fundersnot available
KeywordsPsychologyWell-beingWorking hoursSocial psychologyDevelopmental psychologyLabour economics

Abstract

fetched live from OpenAlex

While existing research indicates that "sandwiched" employees (those with both childcare and eldercare demands) have lower wellbeing than employees with only eldercare demands, there is little understanding how childcare and eldercare demands interact to create those differences. Drawing on two studies, we hypothesize childcare demands amplify the negative impact of eldercare demands on wellbeing. Study 1 operationalizes childcare as a dichotomous variable (i.e., has childcare or not), and examines the relationship between hours per week in eldercare and wellbeing for two groups of employees: those with eldercare and those in the sandwich generation. Study 2, which operationalizes childcare as a continuous variable (i.e., hours in childcare per week), explores how time in childcare moderates the relationship between time in eldercare and wellbeing. Findings show time in eldercare is negatively associated with wellbeing, and the impact of childcare on the relationship between time in eldercare and wellbeing is dependent on how one operationalizes wellbeing and childcare constructs.

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)0.006
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 score0.045
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
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 integrity0.0000.001
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 head0.057
GPT teacher head0.359
Teacher spread0.301 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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