The Effectiveness of Neuro-Linguistic Programming (NLP) on Shooters’ Mental Skills and Shooting Performance
Bibliographic record
Abstract
Background: Regarding to the importance of positive effectiveness of psychological interventions on promoting mental skills and improving the performance and the important role of mental factors in improving athletes’ optimal performance, NLP techniques with claims of magical powers should not be overlooked. However, a few studies have been conducted to investigate the effectiveness of unique NLP evidenced-based practices on mental skills and performance in the sports fields, especially shooting. Objectives: This study aimed to investigate the effectiveness of neuro-linguistic programming (NLP) techniques on shooters’ mental skills and their shooting performance. Methods: This quantitative study is a semi-empirical research with the pretest-posttest design. The participants were 24 male Iranian skilled shooters in 10-meter air rifle discipline with an average age of 24 ± 8 years. They were matched and divided into control and experimental groups. While the experimental group participated in 8, 2.5-hour sessions to receive NLP techniques, the control group did their routine activities. Before and after the intervention, shooters’ mental skills were measured by Ottawa Mental Skills Assessment Tool (OMSAT-3) and their performance in 2 different “ordinary” and “under pressure” conditions was measured based on the score recording rules of the Islamic Republic of Iran Shooting Sports Federation (IRISSF). Data were analyzed using a two-way (time × group) repeated measure MANOVA. Results: The results of this study showed that NLP techniques improve shooters’ mental skills and their performance in both ordinary and under pressure conditions of the competition. Conclusions: As there is a positive significant of the effectiveness of NLP on shooters’ mental skills and their performance, NLP techniques are suggested to be used to improve these two factors.
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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 | 0.001 | 0.004 |
| 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".