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Record W4417400787 · doi:10.1080/24711616.2025.2600350

Boredom in Sport Practice: Using Control-Value Theory to Test Relationships Between Cognitive Appraisals, Boredom, and Perceived Success

2025· article· en· W4417400787 on OpenAlexaffabout
Nya G. Derkach, Patti C. Parker

Bibliographic record

VenueInternational Journal of Kinesiology in Higher Education · 2025
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsTest (biology)BoredomCognitionPerceptionSocial cognitive theory

Abstract

fetched live from OpenAlex

Boredom is a deactivating emotion that is gaining attention in sport research. We draw from the Control-Value Theory of Achievement Emotions (CVTAE) to examine the relationships between cognitive appraisals, boredom, and perceived success, and test if the relationships are moderated by competitive playing experience. A cross-sectional survey was administered to 179 Canadian athletes aged 18–35 participating in competitive sports. Using moderated-mediation regression analyses, we tested if perceived value and control in practice are related to boredom; if a value-success relationship is mediated by boredom; and if it is moderated by competitive playing experience. We found perceived value was negatively linked to boredom (β = -.22) and associated with higher perceived success a (β = .55), but the relationship was not mediated by boredom. A significant Perceived Value x Years of Playing Experience interaction emerged; and after probing the interaction, we found the relationship between perceived value and boredom was strongest for athletes with high playing experience (β = -.36) relative to less experienced athletes. The value-boredom-success mediation relationship was not significant at levels of the moderator. Although more research is needed beyond this correlational design, our study suggests using CVTAE to understand the links between appraisals and emotions may be relevant in sport achievement domains, such as in sport practices.

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)0.005
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 score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.394
Teacher spread0.344 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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