A Case Study of Junior Elite Tennis Players' and their Parent’s Self-Talk
Bibliographic record
Abstract
Automatic self-talk of elite athletes provides valuable insight into their emotional experience and self-regulation strategies in competition. To date, there is a shortage of research examining elite junior athletes’ automatic self-talk in competition through a qualitative lens. Despite parents’ key role in the well-being and performance of their child, there is no study about junior elite athletes’ and their parents’ self-talk during a competition. Hence, the aim of this study is to examine the content of elite junior tennis players’ automatic self-talk as well as the content of their parents’ self-talk regarding their emotions during important matches. In each of the two cases under investigation, individual in-depth interviews were conducted with a tennis player and his or her most dedicated parent. The results were analyzed using Yin’s (2014) multiple-case study strategy and Polkinghorne’s (1995) narration inquiry strategy. An analysis of automatic self-talk content was conducted individually for each case, followed by an intra-case and cross-case analysis. The results reveal that each player’s and parent’s automatic self-talk is related to their own subjective emotional experience during the matches. The findings highlight similarities in athletes’ and parents’ self-talk patterns, reflecting the potential influence of parents in athletes’ performance pressure and their goal-directed self-talk strategies. The differences observed between the self-talk of players and their parents demonstrate the relevance of examining their profiles to better understand the origin of individual differences in self-talk.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".