The Application of Neuro Linguistic Programming (NLP) on Cognitive Function and Stress Reduction
Bibliographic record
Abstract
An approach or analysis of a problem that is expressed in the description of cognitive function and the reduction in stress levels experienced by athletes as a result of a pandemic situation so that there is no decrease in athlete's performance, several alternatives are needed that can maintain the athlete's condition. Based on the problems mentioned, the aim of this research is to evaluate the application of Neuro-Linguistic Programming (NLP) on cognitive function and stress reduction of basketball athletes. The method used in this research is the experimental method. The population in this study was 25 athletes in the sport of basketball at the Crows club. The number of samples is the total population of 25 athletes and the technique used is purposive sampling. The instrument for cognitive function uses the Montreal Cognitive Assessment (MoCA) and the instrument used for the level of stress is the DASS-21 Questionnaire (Depression Anxiety Stress Scale) developed by Lovibond. S. H and Lovibond. P. H (1995). The results of the study regarding the effect of the application of Neuro-Linguistic Programming (NLP) on cognitive function and stress levels can be concluded that there is a significant effect indicated by a significance value (sig.) Of 0.000, less than 0.05 (0.000 <0.05). The different test between the application of Neuro-Linguistic Programming (NLP) on cognitive function and stress reduction found that there was a difference in the effect between the application of Neuro-Linguistic Programming (NLP) on cognitive function and stress reduction marked with a 2-way (t-tailed) significance value of 0.000 < 0.05. Overall, the conclusion of this study is that the application of psychological training, especially the use of the Neuro-Linguistic Programming (NLP) method have positive effect on cognitive function and stress levels of basketball athletes during the COVID-19 pandemic.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".