Savouring with elite adolescent tennis players: a feasibility study
Bibliographic record
Abstract
Adolescent tennis athletes can struggle with mental well-being and emotional regulation during practices and competitions (Lauer et al., 2020). One strategy to control emotions and boost levels of happiness is by savouring positive experiences (Bryant, 2021; Hurley & Kwon, 2012). Savouring is a form of adaptive emotion regulation involving the intentional and deliberate up-regulation of positive affect (Gregory et al., 2023). Savouring-focused interventions have been shown to enhance savouring behaviours and promote psychological well-being (Klibert et al., 2022; Smith & Hanni, 2019). This study for the first time examined the feasibility of a novel savouring-focused intervention with elite adolescent tennis athletes. Participants were high performance adolescent tennis athletes (N = 14) recruited from Taylor Tennis Academy and the Manitoba Provincial and Canada Games Teams. Athletes participated in a savouring intervention in which they (a) completed 3 online surveys (pre-intervention, post-intervention, and 30-day post-intervention) (b) attended an in person savouring intervention, and (c) tracked 7 days of savouring. A subset of athletes (n = 5) also participated in semi-structured exit interviews. Exploratory results showed that the savouring-focused intervention increased savouring scores and positive emotions, and decreased stress levels and negative emotions. Interviewed athletes reported that savouring was beneficial and planned to continue to use savouring in future. This feasibility study gives preliminary evidence that a savouring-intervention is feasible and acceptable to elite adolescent tennis athletes. Future savouring- focused interventions should be explored within elite sport contexts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".