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Perfectionism and performance in sport: Exploring non-linear relationships with track and field athletes

2023· article· en· W4388108649 on OpenAlexaff
Sanna M. Nordin‐Bates, Daniel J. Madigan, Andrew P. Hill, Luke F. Olsson

Bibliographic record

VenuePsychology of sport and exercise · 2023
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsYork UniversityUniversity of Toronto
FundersCentrum för idrottsforskningVetenskapsrådet
KeywordsAthletesPerfectionism (psychology)PsychologyTrack and field athleticsSample (material)Linear relationshipSocial psychologyClinical psychologyDevelopmental psychologyPhysical therapyStatisticsMathematicsMedicine

Abstract

fetched live from OpenAlex

The relationship between perfectionism – perfectionistic strivings and perfectionistic concerns – and athletic performance is contested and inconsistent. The present study explored the possibility that one explanation for this inconsistency is the assumption that the relationship is linear. In two samples, we tested alternative non-linear relationships between perfectionism and real-world competitive athletic performance. Sample one comprised 165 Swedish track and field athletes (57 % competing in female category, 42 % in male category; Mage = 16.93 years) and sample two comprised 157 British track and field athletes (55 % competing in female category, 43 % in male category; Mage = 18.42 years). Testing for linear and non-linear relationships, we found a quadratic effect whereby higher perfectionistic strivings had both positive increasing (i.e., U-shape; sample 1) and positive decreasing (i.e., inverted U-shape; sample 2) relationships with performance. We conclude that there may be circumstances when perfectionistic strivings contribute to better and worse sport performance, and that this relationship can be curvilinear.

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)0.000
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 score0.013
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.301
Teacher spread0.251 · 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
Published2023
Admission routes1
Has abstractyes

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