Differences in mental toughness across types of contact
Bibliographic record
Abstract
Objective: To determine differences in mental toughness between sports involving differing types of contact. Background: Mental toughness is a concept that has been linked with superior performance. It is considered a multidimensional construct that allows competitors to generally cope better than their opponents with many sport related demands. Research Design: This study utilized an observational cross-sectional design. Participants: The sample for this study included 159 varsity and club athletes (males = 77, females = 82) aged 18-33 (M = 20.23, SD = 2.05) from a variety of sport teams. Independent Variable: This investigation used type of contact as an independent variable. All sports were classified according to three levels: non-contact, contact, and collision. Outcomes: Mental toughness was measured using the Sport Mental Toughness Questionnaire (SMTQ). It consists of 14 items relating to three subscales of confidence, control, and constancy, and a global score consisting of all 14 items. Analysis: A one way ANOVA was used to assess differences between types of contact on all scales of the SMTQ. Results: Significant differences were found for the control (p = 0.002), confidence (p = 0.002), and global (p < 0.001) scales. On all of these scales, collision and contact sports scored significantly lower than non-contact sports. No significant differences were found between contact and collision sports. Conclusion: It appears that athletes engaging in collision and contact sports demonstrate less control, confidence, and overall mental toughness, than their non-contact counterparts while no differences in constancy were found.Acknowledgments: Dr. Tak Fung
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".