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Record W4401057761 · doi:10.1080/02614367.2024.2383462

More is better? Family leisure involvement and individual leisure satisfaction among Chinese adult workers

2024· article· en· W4401057761 on OpenAlexaff
Ying Zhao, Dantian Xu, Jingjing Gui

Bibliographic record

VenueLeisure Studies · 2024
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsLeisure satisfactionLeisure activityLeisure studiesSociology of leisureLeisure timePsychologyLeisure industrySociologySocial psychologyMedicinePhysical activityRecreationTourismGeographyPolitical scienceSocial sciencePhysical therapy

Abstract

fetched live from OpenAlex

Family leisure activities have been gaining increasing attention in recent years. This study aims to identify the characteristics of family leisure involvement among adult workers in urban China, explore the relationship between family leisure and leisure satisfaction, and further analyse the possible interrelationship between core and balance family leisure activities. Paper-and-pencil questionnaire surveys of activity diaries were collected from 519 residents of Zhongshan, a prefecture-level city in China. Linear regression analysis suggested that balance family leisure activities were not associated with individual leisure satisfaction, while core family leisure had a negative impact. Core family leisure also moderated the relationship between balance family leisure and personal leisure satisfaction. A moderate amount of core family leisure combined with balance family leisure increased leisure satisfaction. This study contributes to the core and balance theory of family leisure functioning by exploring the interrelationship between the two types of family leisure activities in a non-Western context.

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)0.001
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 score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.333
Teacher spread0.299 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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