The benefits of instructional and motivational self-talk during tennis service
Bibliographic record
Abstract
The purpose of this study was to examine the effects of instructional and motivational self-talk on precision and power during tennis service of novice tennis players. Self-talk is defined as "(a) verbalizations or statements addressed to the self; (b) multidimensional in nature; (c) having interpretive elements association with the content of statements employed; (d) is somewhat dynamic; and (e) serving at least two functions; instructional and motivational, for the athlete" (Hardy, 2006).The hypotheses were: both forms of self-talk will improve performance, instructional self-talk will result in a greater increase in skill precision and motivational self-talk will result in a greater increase in service speed. Speed and accuracy of participants' serves were measured. Two one-way analyses of variances were conducted to look at the relationship between the change in speed and precision of participants' tennis service from pre- to post-intervention. The self-talk interventions did have an impact on serve speed, motivational self-talk resulting in higher speeds than the control group and instructional self-talk resulting in lower speeds (the ANOVA was significant (?= .05), F(2,257) = 19.151 , p =.00). However, self-talk interventions did not have an impact on serve precision (the ANOVA was not significant (?= .05), F(2,257) = .907, p = .405.). The current findings support the results of Hatzigeorgiadis et al. (2010) motivational self-talk improves performance on power tasks involving gross muscle movements.
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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 | 0.001 |
| 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".