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Record W2587170892

Mental toughness, mental skills, and hardiness in team and individual athletes

2016· article· en· W2587170892 on OpenAlex
Georgia Ens, David M. Paskevich, Ben Vandervies

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Exercise, Movement, and Sport · 2016
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMental toughnessHardiness (plants)AthletesPsychologyClinical psychologyMental healthPhysical therapyPsychiatryMedicine
DOInot available

Abstract

fetched live from OpenAlex

Background: Superior sport performance has been attributed to a variety of factors including mental toughness, mental skills, and hardiness (Gould, Dieffenbach, & Moffett, 2002). However, it has been suggested that these factors may vary between participants in different types of sport (Nicholls, Polman, Levy, & Backhouse, 2009). Research Design: Cross-sectional survey design. Participants: 159 varsity and club athletes from ages 18-33 (M= 20.23 SD = 2.05) were recruited from multiple sports. Measures: Test of Performance Strategies (TOPS) measured mental skills. The Sport Mental Toughness Questionnaire (SMTQ) measured mental toughness. The Dispositional Resilience Scale (DRS-15) measured hardiness. Procedures: Independent t-tests were conducted to assess the difference between team and individual athletes on mental skills, mental toughness, and hardiness subscales found in their respective questionnaires. Results: On the TOPS, significant differences were found between team and individual sport athletes on practice activation (p=0.018), practice relaxation (p=0.004), competition activation (p

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.

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 score0.054
Threshold uncertainty score0.404

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 head0.012
GPT teacher head0.256
Teacher spread0.243 · 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