Mental toughness, mental skills, and hardiness in team and individual athletes
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".