Mental Skills of an Athlete as a Resource of Professional Longevity in Sport
Bibliographic record
Abstract
The aim of the study was to assess associations between dimensions of mental skills, psychological readiness and predictors of career longevity among current athletes. Mental skills dimensions were measured with the Ottawa Mental Skills Assessment Tool (OMSAT). The psychological predictors of athletic career longevity were measured with the Athletic Coping Skills Scale-28 (the ACSI-28) producing scores for seven coping skills and with the Competitive State Anxiety Inventory-2 (CSAI-2) which produced scores for confidence in competition. Overall, 253 current athletes (average age 22 years) of various competitive levels (classified to 5 groups according to the national sport classification system) participated in the study. The results showed that coping skills and confidence in competition positively associated with various range of mental skills dimensions. The competitive level was related to stress reactions, fear control, focusing, refocusing and imagery coping skills. The results suggest that some mental skills may came with experience, while the development of other mental skills may require interventions to reduce the likelihood of early termination of the professional career and achieve career longevity. The results also discussed in terms of a holistic perception of sports psychology and focuses not only on performance, but also on the well-being and sustainable development of the athletes.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".