MétaCan
Menu
Back to cohort
Record W3080087082 · doi:10.1177/2156869320947463

Financial Strain and Psychological Distress: Do Strains in the Work-Family Interface Mediate the Effects?

2020· article· en· W3080087082 on OpenAlexafffundabout
Lei Chai, Scott Schieman, Alex Bierman

Bibliographic record

VenueSociety and Mental Health · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of CalgaryUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsWork–family conflictPsychologyCausality (physics)DistressSocial psychologyRole conflictWork (physics)Psychological distressDevelopmental psychologyClinical psychologyMental healthPsychiatry

Abstract

fetched live from OpenAlex

Analyzing three waves of the Canadian Work Stress and Health Study with cross-lagged models, we asked: (1) How do two distinct directions of strain in the work-family interface—work-to-family conflict and family-to-work conflict—mediate the relationship between financial strain and psychological distress? and (2) Is reverse causality a possibility in these dynamics? Our results indicate that work-to-family conflict at Wave 2 mediates the relationship between financial strain at Wave 1 and distress at Wave 3, but family-to-work conflict does not function as a mediator. Financial strain is therefore indirectly associated with subsequently higher levels of distress. In tests for reverse causality, we found little evidence that distress is associated with subsequently higher levels of financial strain—and neither work-to-family conflict nor family-to-work conflict at Wave 2 mediates that relationship. We interpret our findings within the conceptual and empirical ideas associated with stress proliferation, social causation, and social selection/drift.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.449
Threshold uncertainty score0.892

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.040
GPT teacher head0.348
Teacher spread0.308 · 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

Citations17
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueSociety and Mental HealthSame topicWork-Family Balance ChallengesFrench-language works237,207