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Record W2562674202 · doi:10.5539/ijps.v9n1p47

Daily Fluctuations in Office-Based Workers’ Leisure Activities and Well-Being

2016· article· en· W2562674202 on OpenAlexaffvenue
Julie Ménard, Annie Foucreault, Célestine Stevens, Sarah‐Geneviève Trépanier, Paul E. Flaxman

Bibliographic record

VenueInternational Journal of Psychological Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsEveningBedtimeMoodPsychologyPhysical activityLeisure timeActivities of daily livingLeisure activityOffice workersWell-beingSample (material)Social psychologyDevelopmental psychologyGerontologyPhysical therapyPsychiatryMedicinePsychotherapist

Abstract

fetched live from OpenAlex

This day-level study examines links between the amount of leisure time devoted to social, physical, and low-effort activities after the workday and affective well-being at bedtime. A sample of 95 office-based workers completed surveys over four consecutive days at bedtime (380 data points). Results revealed a within-person effect of leisure activity on daily affective well-being. Participants consistently reported enhanced mood before sleep on days when they spent more hours engaging in physical and social activities compared to their personal average number of hours spent on these activities across the four days of the study. However, on days when more hours were spent on low-effort activity, participants consistently reported decreased positive emotions. This suggests that time allocation to certain leisure activities may better support well-being on a daily basis. Discussion focuses on the implications of these findings for helping individuals enhance their evening leisure experiences by making astute leisure choices.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.393
Teacher spread0.332 · 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 teacher head, 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

Citations10
Published2016
Admission routes2
Has abstractyes

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