Do Elite Coaches from Disability Sport Use Psychological Techniques to Improve Their Athletes’ Sports Performance?
Bibliographic record
Abstract
Goal-setting, imagery, relaxation and self-talk are psychological strategies crucial for successful psychological preparation and consequently for the improvement of the athlete’s sport performance. The coaches have an important role in the implementation of psychological skills training and may contribute to increase the use of psychological strategies by their athletes. Therefore, the purpose of this study was to examine the importance assigned to a group of psychological strategies (i.e., goal-setting, imagery, relaxation and self-talk) and its use in practice and competition setting by top elite coaches from disability sport. In-depth semi-structured interviews were conducted on ten elite Portuguese coaches. Content analysis was the qualitative methodology used for data analysis. Globally, the coaches acknowledge the importance of all four psychological strategies approached. However, the examination of the coaching routines on the application of psychological strategies suggested an undeveloped use of most of the strategies, specifically in the practice setting. Relaxation and self-talk were the most underused strategies. All the coaches reported the use of goal-setting in both the practice and competition setting. Overall, the present findings raise concerns about the effective contribution of Portuguese elite coaches for the development of successful psychological preparation among athletes with disabilities.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".