Mindfulness Components And Interactions Leading To The Performance During A Competition Amongst University Cross-country Runners
Bibliographic record
Abstract
The performance during a competition among endurance athletes may be distressed by psychological parameters. PURPOSE: To measure the effect of the components of competitive anxiety and mindfulness on the performance of a 3000m race. METHODS: A varsity distance runner group (University of Quebec at Montreal) were invited to participate at the recruiting time trial races to be part of the cross country varsity team. A total of 20 runners participated, 10 women (25.9±7.0 years old; 22.2±1.8 BMI) and 10 men (23.2±2.4 years old; 22.6±1.6 BMI). The recruitment trial was a 3000m foot race on the offside track. Two groups were formed by randomized distribution for each gender group. Before warm-up, participants completed the French versions of the Competitive State Anxiety Inventory (CSAI-2R) and Five Facets Mindfulness Questionnaire (FFMQ). Participants for the time trials were equipped with a biomonitor (BioHarness 3; Zephyrs Technology Corp., Annapolis, Md.) that measured heart rate, respiratory rate and running speed. The total time and the time split for each 400m were noted. After the 3000m trial, the participants rated their perception of effort during the race (modified Borg Scale). RESULTS: The average time of the 3000m race for men and women was 639.8±43.3 and 828.9±79.3 seconds, respectively. The Pearson correlation analysis revealed a significant positive link between the self-esteem score (CSAI-2R) and the mindfulness (FFMQ) score (r2=0.27; p<0.05) and the experience description (FFMQ) factor (r2=0.33; p<0.01). Additionally, the number of running years’ experience was positively correlated with self-confidence (CSAI-2R) and negatively to non-responsiveness (FFMQ) to private events (r2=0.28; p<0.05; r2=0.33, p<0.05 respectively). In addition, the number of running practice hours per week was negatively correlated with competitive anxiety (CSAI-2R), observation factors (FFMQ) and non-responsiveness (FFMQ) to private events (r2=0.43, p<0.01; r2=0.27, p<0.05; r2=0.30, p<0.05 respectively). CONCLUSIONS: A connection between anxiety of competition and the components of mindfulness seem interesting for an intervention by mindfulness to modify psychological factors before, during and after competitive events and training sessions.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".