Can Savouring be used in Elite Sport to Benefit Athletes?
Bibliographic record
Abstract
Savouring is a mental strategy involving the deliberate and intentional upregulation of positive affect (Bryant, 2021). This happiness amplification technique boosts and maximizes preferred emotions making them last. Savouring outside of sport has shown benefits in health and well-being, gratefulness, appreciation, strengthening of relationships, building of competence, and resilience (Borelli et al., 2020; Klibert et al., 2022; Smith et al., 2019). Using savouring develops adaptive emotional regulation (Gregory et al., 2023) and may be beneficial for creating positivity in elite athletes during the difficult mental struggles and uncertainty of experience that occurs within high-pressure sport environments. This study examined the feasibility of a savouring-focused intervention in elite adolescent tennis athletes. These elite athletes (N = 14) were recruited from Taylor Tennis Academy, the Manitoba Provincial and Canada Games Teams. Athletes participated in a savouring-focused intervention in which they attended an in-person psychoeducational session, tracked savouring for 7 days, and completed 3 online surveys. Exploratory surveys were conducted pre-intervention, post-intervention, and 30-day post-intervention. A subgroup of athletes (n = 5) participated in a semi-structured interview. Results showed that the savouring-focused intervention increased savouring scores and positive emotions, while decreasing stress levels and negative emotions. Elite adolescent athletes savoured tennis most by 1) imagining success 2) being grateful and 3) showing good feelings. Athletes reported that learning savouring was beneficial and plan to incorporate this mental strategy into their routines. This study provides preliminary evidence that savouring-focused interventions should be implemented within elite sport.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".