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CROSS‐NATIONAL DIFFERENCES IN RELATIONSHIPS OF WORK DEMANDS, JOB SATISFACTION, AND TURNOVER INTENTIONS WITH WORK–FAMILY CONFLICT

2007· article· en· W2145326460 on OpenAlexaff
Paul E. Spector, Tammy D. Allen, Steven Poelmans, Laurent Lapierre, Cary L. Cooper, O'DRISCOLL MICHAEL, Juan I. Sánchez, Nureya Abarca, Matilda Alexandrova, Barbara Beham, Paula Brough, Pablo Ferreiro, Guillermo Fraile, Chang-qin Lu, Luo Lu, Ivonne Moreno-Velázquez, Milan Pagon, Horea Pitariu, В. Саламатов, Satoru Shima, Alejandra Suarez Simoni, Oi Ling Siu, Maria Widerszal‐Bazyl

Bibliographic record

VenuePersonnel Psychology · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsychologyModerationCollectivismSocial psychologyJob satisfactionCluster (spacecraft)IndividualismWork–family conflictWork (physics)Latin AmericansPolitical science

Abstract

fetched live from OpenAlex

A study of work interference with family (WIF) among managers is described, contrasting four clusters of countries, one of which is individualistic (Anglo) and three of which are collectivistic (Asia, East Europe, and Latin America). Country cluster (Anglo vs. each of the others) moderated the relation of work demands with strain‐based WIF, with the Anglo country cluster having the strongest relationships. Country cluster moderated some of the relationships of strain‐based WIF with both job satisfaction and turnover intentions, with Anglos showing the strongest relationships. Cluster differences in domestic help were ruled out as the possible explanation for these moderator results.

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)0.003
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.096
GPT teacher head0.361
Teacher spread0.266 · 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

Citations478
Published2007
Admission routes1
Has abstractyes

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