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Record W2120113376 · doi:10.1123/jsep.32.3.298

The Ups and Downs of Coping and Sport Achievement: An Episodic Process Analysis of Within-Person Associations

2010· article· en· W2120113376 on OpenAlexafffund
Patrick Gaudreau, Adam R. Nicholls, Andrew R. Levy

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDisengagement theoryPsychologyCoping (psychology)DistractionCompetitive sportMultilevel modelDevelopmental psychologySocial psychologyClinical psychologyCognitive psychologyGerontology

Abstract

fetched live from OpenAlex

This study examined the relationship between coping and sport achievement at the within-person level of analysis. Fifty-four golfers completed diary measures of coping, stress, and sport achievement after six consecutive rounds of golf. Results of hierarchical linear modeling revealed golfers' episodic task-oriented coping and disengagement-oriented coping were associated, respectively, with their better and worst levels of subjective and objective achievement. Distraction-oriented coping was not significantly associated with achievement. These results were obtained after accounting for between-subjects differences in ability level and for within-person variations in perceived stress across both practice and competitive golf rounds. These results contribute to an emerging literature on the relationship between coping and sport achievement, and highlight the promises of an episodic process model of sport achievement to understand the transient self-regulatory factors associated with within-person variations in athletic achievement.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.332
Teacher spread0.311 · 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

Citations84
Published2010
Admission routes2
Has abstractyes

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