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Record W4392125607 · doi:10.1080/00222216.2024.2305767

Effects of online leisure education on leisure behaviors and experiences among university students

2024· article· en· W4392125607 on OpenAlexaff
Shintaro Kono, Seung Jin Cho, John Dattilo, Shinichi Nagata

Bibliographic record

VenueJournal of Leisure Research · 2024
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLeisure timePsychologyRecreationLeisure activityLeisure studiesSociology of leisureSociologyLeisure satisfactionSocial psychologyPhysical activitySocial scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

Leisure education (LE) is designed to help foster positive leisure attitudes, identify leisure constraints and opportunities, and develop leisure skills and knowledge. Although LE is effective in many populations, its delivery has been predominantly in-person. We conducted an experimental study of a fully online LE intervention (ONLEI) with 96 university students. Our 8-week intervention occurred on a Moodle platform, featuring pre-module and post-module quizzes, information videos on YouTube, learning activities, online discussion forum, and private journaling. Our fidelity checks suggested intervention participants, on average, watched 63% of the videos, while also increasing their quiz scores. Rates for forum and journal engagement were moderate to low. Multilevel linear modeling indicated the ONLEI group showed statistically better trends for leisure participation, leisure satisfaction, and frustration of competence and relatedness needs in leisure, than the control group. However, the groups did not differ in terms of autonomy frustration and needs satisfaction in leisure.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.023
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.439
Teacher spread0.399 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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