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Record W4386090526 · doi:10.1080/00222216.2023.2237509

Examining basic psychological need frustration’s relevance to leisure: A case of Chinese international students during the COVID-19 pandemic

2023· article· en· W4386090526 on OpenAlexaff
Shintaro Kono, Jingjing Gui, Yeanna He, Kimberly A. Noels

Bibliographic record

VenueJournal of Leisure Research · 2023
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyAutonomyRecreationCompetence (human resources)FrustrationLife satisfactionAnxietySocial psychologyPandemicBasic needsSelf-determination theoryCoronavirus disease 2019 (COVID-19)Political scienceMedicinePsychiatry

Abstract

fetched live from OpenAlex

Although Basic Psychological Need Theory (BPNT) has guided the research on leisure’s impacts on subjective well-being, past studies focused on its positive side: need satisfaction. The purpose of this study is to investigate whether need frustration—where individuals are actively prevented from satisfying their needs for autonomy, competence, and relatedness—is relevant to leisure, within the case of Chinese international students during the COVID-19 pandemic. Data from 444 students were collected through a cross-sectional online survey. A series of hierarchical regressions showed that need frustration—especially competence frustration—in leisure predicted leisure satisfaction, global life satisfaction, anxiety, and depression, above and beyond need satisfaction in leisure. While the results generally support BPNT, we also discuss them in relation to the unique life domain of leisure, Chinese culture, and post-pandemic life. We maintain that need frustration makes BPNT a useful theoretical framework to examine both negative and positive experiences within 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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
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)0.0020.000

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 head0.235
GPT teacher head0.515
Teacher spread0.280 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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