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Record W2002075237 · doi:10.1037/a0021035

Unraveling the daily stress crossover between unemployed individuals and their employed spouses.

2010· article· en· W2002075237 on OpenAlexaff
Zhaoli Song, Maw‐Der Foo, Marilyn A. Uy, Shuhua Sun

Bibliographic record

VenueJournal of Applied Psychology · 2010
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of ChinaNational University of Singapore
KeywordsStressorPsychologyCrossoverDistressCrossover studyUnemploymentSocial psychologyClinical psychologyMedicineEconomics

Abstract

fetched live from OpenAlex

This study examined the dynamic relationship of distress levels between spouses when one is unemployed (and looking for a job) while the other is engaged in full-time employment. Using the diary survey method, we sampled 100 couples in China for 10 days and tested a model comprising three stress crossover mechanisms: the direct crossover, the mediating crossover, and the common stressor mechanisms. Results supported the direct crossover and common stressor mechanisms. Other stressors (e.g., work–family conflict and negative job search experience) were also related to distress of the unemployed individuals and their employed spouses. Additionally, we found a three-way interaction involving gender, marital satisfaction, and distress levels of employed spouses. We discuss how the study contributes to the unemployment and stress crossover literatures.

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)0.002
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.049
GPT teacher head0.414
Teacher spread0.364 · 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

Citations166
Published2010
Admission routes1
Has abstractyes

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