Self-compassion and motivational climate as promotive factors of resilience in youth athletes
Bibliographic record
Abstract
Resilience has been identified as a key psychological characteristic of successful and well-adapted athletes and is a critical component in athlete development (Rees et al., 2016). Promotive factors of resilience include assets, or skills within an individual, and resources, which is support outside the individual. The purpose of this study was to examine self-compassion and motivational climate as potential promotive factors of resilience in youth athletes. Athletes (N = 117; 87 female; M age = 14.73 years, SD = 1.89) completed the Self-Compassion Scale, Motivational Climate Scale for Youth, Connor-Davidson Resilience Scale, Flourishing Scale, and Short Warwick-Edinburgh Mental Well-being Scale. Self-compassion (r = .57, p< .01) and a mastery motivational climate (r = .21, p< .05) were related to resilience. Self-compassion was also related to flourishing (r = .66) and mental well-being (r = .64; both p< .01). Mastery climate was related to mental well-being (r = .19, p< .05). While the present data are cross-sectional in nature and cannot imply causation, past research suggests these promotive factors are modifiable and can be targeted through intervention and promotion efforts (Mosewich et al., 2013; Smith et al., 2007). Motivational climate has been explored in previous research as a resource for promoting resilience (e.g., Vitali et al., 2015); however, self-compassion represents a potential asset that has been overlooked in the sport context in terms of resilience development. Further consideration of the role of self-compassion in fostering resilience among athletes may support management of sport demands and the development and maintenance of well-being.Acknowledgments: Supported by the Social Sciences and Humanities Research Council of Canada
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".