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Record W2161095413 · doi:10.7202/012157ar

Job Stress, Depression and Work-to-Family Conflict

2006· article· en· W2161095413 on OpenAlexaffvenue
Jean E. Wallace

Bibliographic record

VenueRelations industrielles · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsModerationWork–family conflictSpousePsychologySocial supportJob stressSocial psychologyWork (physics)Job controlControl (management)Job analysisJob satisfactionManagementSociologyEconomicsEngineering

Abstract

fetched live from OpenAlex

In this paper, the Job Demand-Control (JDC) model is used to predict depression and work-to-family conflict for married lawyers working full-time. The objectives of this paper are: (1) to determine whether the JDC model applies to work-to-family conflict; (2) to incorporate domain-specific job demand and job control variables; and (3) to examine a wider array of different forms of social support. First, the JDC model also helps explain work-to-family conflict. Second, domain-specificity does not appear key to documenting the buffering effects for job control. Third, spouse’s support of one’s career has the strongest main effect on both depression and work-to-family conflict, whereas coworker support functions as a moderator of lawyers’ job demands and has both buffering and amplifying effects. This paper closes by discussing the possible conditions under which members of support systems may transfer or exacerbate stress effects rather than alleviate them.

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)0.005
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
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.282
Teacher spread0.250 · 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

Citations102
Published2006
Admission routes2
Has abstractyes

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