Mental toughness, hardiness, and mental skills in Canadian university athletes
Bibliographic record
Abstract
Background: Mental toughness (MT) is a multifaceted construct that is considered a prominent factor in sport performance (Gould, Dieffenbach, & Moffett, 2002). It consists of an ability to cope better than opponents with sport demands, and remain focused and confident under pressure (Jones, Hanton, & Connaughton, 2002). Previous findings have suggested that MT may be related to both hardiness (Clough, Earle, & Sewell, 2002) and mental skills (Crust & Azadi, 2010). Research Design: Cross-sectional design. Participants: The sample included 159 varsity and club athletes (males = 77, females = 82) aged 18-33 (M = 20.23, SD = 2.05) from a variety of sports. Measures: The Sport Mental Toughness Questionnaire (SMTQ) was used to measure MT. Hardiness was measured using the 15-item Dispositional Resilience Scale (DRS-15). Mental skills were measured using the Test of Performance Strategies (TOPS). Procedures: Pearson product-moment correlations were used to assess the relationships between subscales of MT, hardiness, and mental skills. Results: Significant low to moderate correlations (r = 0.18 to r = 0.63) were found between most SMTQ and TOPS subscales in both practice and competition. Significant low to moderate correlations (r = 0.17 to r = 0.54) were also found between all DRS-15 and SMTQ subscales except challenge and constancy, and both control subscales. Conclusion: The magnitude of correlations between hardiness, mental skills, and MT suggests these constructs are related, yet distinct. These correlations help demonstrate the convergent validity of the SMTQ, as well as potentially exhibit qualities of mentally tough performers in this population.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".