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Record W2116035830 · doi:10.1177/0022146511419204

When Hard Times Take a Toll

2012· article· en· W2116035830 on OpenAlexaff
Marisa Young, Scott Schieman

Bibliographic record

VenueJournal of Health and Social Behavior · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Toronto
FundersNational Institute for Occupational Safety and Health
KeywordsDistressPsychologyContext (archaeology)Association (psychology)TollSocial psychologyWork (physics)Clinical psychologyDevelopmental psychologyMedicinePsychotherapistGeography

Abstract

fetched live from OpenAlex

Using two waves of data from a national survey of working Americans (N = 1,122), we examine the associations among economic hardship, negative life events, and psychological distress in the context of the family-work interface. Our findings demonstrate that family-to-work conflict mediates the effects of economic hardship and negative events to significant others on distress (net of baseline distress and hardship). Moreover, economic hardship and negative events to significant others moderate the association between family-to-work conflict and distress. While negative events to others exacerbate the positive effect of family-to-work conflict on distress, we find the opposite for economic hardship: The positive association between hardship and distress is weaker at higher levels of family-to-work conflict. These patterns hold across an array of family, work, and sociodemographic conditions. We discuss how these findings refine and extend ideas of the stress process model, including complex predictions related to processes of stress-buffering, resource substitution, and role multiplication.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0340.005

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 head0.078
GPT teacher head0.374
Teacher spread0.296 · 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

Citations89
Published2012
Admission routes1
Has abstractyes

Explore more

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