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Record W4406245039 · doi:10.1080/02614367.2025.2451286

Moderating effect of leisure satisfaction on the relationship between work-life conflict and life satisfaction

2025· article· en· W4406245039 on OpenAlexaffabout
Mingjie Gao, Nanxi Yan, Bryan Smale

Bibliographic record

VenueLeisure Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLife satisfactionLeisure satisfactionPsychologyJob satisfactionLeisure timeWork (physics)Social psychologyPhysical activityMedicineEngineering

Abstract

fetched live from OpenAlex

This study explores the role of leisure satisfaction as a potential moderator in the relationship between work-life conflict and life satisfaction. Data were drawn from a survey of a stratified random sample of residents living in urban areas of a southwestern region of Ontario, Canada, during the fall of 2022, and focused just on individuals who worked for pay (n = 2,473). After controlling for selected demographic factors, results of hierarchical regression analyses suggest that leisure satisfaction significantly mitigates the negative influence of the conflicts between work and personal life on life satisfaction. The findings advance our understanding of the perceived benefits of leisure in the theoretical framework concerning work-life interference and overall wellbeing. Implications and future study directions are discussed accordingly.

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)0.011
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.086
GPT teacher head0.363
Teacher spread0.277 · 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

Citations5
Published2025
Admission routes2
Has abstractyes

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