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Record W2154572197 · doi:10.1177/002214650604700304

The Nature of Work and the Stress of Higher Status

2006· article· en· W2154572197 on OpenAlexaffabout
Scott Schieman, Yuko Whitestone, Karen Van Gundy

Bibliographic record

VenueJournal of Health and Social Behavior · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsPsychologyWork (physics)Role conflictOccupational stressWork hoursDemographic economicsSocial psychologyDemographySociology

Abstract

fetched live from OpenAlex

Are occupational and work conditions associated with work-to-home conflict? If so, do those associations vary by gender? Among a sample of adults in Toronto, Canada, we found that men and women in higher-status occupations reported higher levels of work-to-home conflict than workers in lower-status jobs. In addition, we observed higher levels of work-to-home conflict among workers who are self-employed and among those with more job authority, demands, involvement, and longer hours. The only significant gender-contingent effect was found for nonroutine work, which is associated positively with work-to-home conflict among men but not women. Higher levels of job demands, involvement, and hours among individuals in higher-status occupations significantly contribute to occupation-based differences in work-to-home conflict. Moreover, despite some overlap, these work conditions have largely independent associations with work-to-home conflict. Results generally support the "stress of higher status " hypothesis among both women and men. Although higher-status positions yield many rewards, such positions are not impervious to inter-role stress, and this stress may offset those rewards.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.345
Teacher spread0.324 · 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

Citations309
Published2006
Admission routes2
Has abstractyes

Explore more

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