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Record W4404642393 · doi:10.1108/pr-05-2024-0424

When families overload careers: the critical role of family-interferes-with-work and boundary management

2024· article· en· W4404642393 on OpenAlexaffabout
Michael Halinski, Laura Gover, Linda Duxbury

Bibliographic record

VenuePersonnel Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsCarleton UniversityVancouver Island UniversityToronto Metropolitan University
Fundersnot available
KeywordsWork (physics)Boundary (topology)Critical management studiesPsychologyManagementOperations managementSociologyPublic relationsBusinessPolitical scienceEconomicsEngineeringSocial science

Abstract

fetched live from OpenAlex

Purpose While there has been growing interest in how personal and work-related factors shape employees’ careers, we know little about how family demands affect career intentions. Drawing from role theory and boundary theory, we examine the indirect effect of family-role overload on career intentions via family-interferes-with-work (FIW), as well as the conditional indirect effect of boundary management on these relationships. Design/methodology/approach Utilizing two waves of panel data that were collected in the third and fourth waves of the pandemic in Canada ( n = 433), we conducted a structural equation model to test our hypotheses. Findings Our analysis reveals that FIW mediates the relationship between family-role overload and (1) career change intention and (2) job turnover intention. The results also indicate that the effect of family-role overload on career intentions via FIW strengthens for employees with a low ability to enact preferred boundaries. Originality/value This research shows the indirect effect of family-role overload on career intentions via FIW. This research also highlights how boundary management can buffer the effects of family-role overload on career intentions.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.798
Threshold uncertainty score0.441

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 studies0.0000.001
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 head0.029
GPT teacher head0.312
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2024
Admission routes2
Has abstractyes

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