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Record W2135272506 · doi:10.1123/jsep.2015-0064

Self-Control Strength Depletion Reduces Self-Efficacy and Impairs Exercise Performance

2015· article· en· W2135272506 on OpenAlexaff
Jeffrey D. Graham, Steven R. Bray

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStroop effectPsychologySelf-efficacyEgo depletionTask (project management)Isometric exerciseSelf-controlSelf-affirmationDevelopmental psychologyPhysical medicine and rehabilitationPhysical therapyCognitionSocial psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the role of task self-efficacy as a psychological factor involved in the relationship between self-control depletion and physical endurance. Participants (N = 37) completed two isometric handgrip endurance trials, separated by a Stroop task, which was either congruent (control) or incongruent (causing depletion). Task self-efficacy for the second endurance trial was measured following the Stroop task. Participants in the depletion condition reported lower task self-efficacy and showed a greater reduction in performance on the second endurance trial when compared with controls. Task self-efficacy also mediated the relationship between self-control depletion and endurance performance. The results of this study provide evidence that task self-efficacy is negatively affected following self-control depletion. We recommend that task self-efficacy be further investigated as a psychological factor accounting for the negative change in self-control performance of physical endurance and sport tasks following self-control strength depletion.

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)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0020.000

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 head0.032
GPT teacher head0.352
Teacher spread0.320 · 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

Citations45
Published2015
Admission routes1
Has abstractyes

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