Interactive effect of mental skills training and anxiety on Indian athlete's performance
Bibliographic record
Abstract
This study examined the mental skill and competitive anxiety level as well as their relationship in Indian athlete’s performance during competition. The totals of thirty eight (38) athletes of International and National level were selected to measure the correlation between variables of Ottawa Mental Skills Assessment Tools (version 3) with one weak difference and the relationship between Competitive State Anxiety Inventory-2D and Ottawa Mental Skills Assessment Tools (version 3) with the help of Pearson’s Correlation. The responses given by athletes on mental skills questionnaire in Hindi and English language, which assessed 48 questions based on foundation skills, psychosomatic skills and cognitive skills. In other hand the responses of athletes on mental skills and competitive anxiety questionnaire in Hindi and English language assessed cognitive anxiety, somatic anxiety and self-confidence. According to the results and finding of this study, it is recommended that coaches must use mental skills during training for their athlete’s performance which helpful for the athlete’s to facilitate the performance and reduce the anxiety level during competition and create a positive approach’s for their goal attainment. rnThis study revealed that Mental Skills are helpful to established positive approaches in athletes in relations to their performance. The statistical analysis uses define the Reliability of Ottawa Mental Skills Assessment Tool -3 skills and relationship between OMSAT-3 and CSAI-2D on Indian population. The Pearson’s Correlation method used with Test-Retest on athletes which measures significant relationship between 3 skills of mental skill tool and Person’s Product Moment Correlation also used on Indian athlete’s performance which measures the significant relationship between mental skills and competitive anxiety.
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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 | 0.000 | 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) | 0.003 | 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".