Self-compassion and psychological skills as predictors of resilience and well-being among youth athletes
Bibliographic record
Abstract
Longitudinal tracking of psychosocial factors will help to understand and support athlete development and well-being (Eime et al., 2015; Vierimaa et al., 2012). The objective of our longitudinal program of research is to examine the trajectories of change in psychosocial skills, resources, resilience, and well-being among adolescent athletes participating in sport programming guided by Canada's Long Term Athlete Development (LTAD) framework. As part of this research, we used latent growth curve modeling to examine psychosocial skills and resources as predictors of well-being and resilience over time. Sixty-eight (23 boys) youth athletes (Mage = 14.80 years at Time 1, SDage = 1.78) completed measures of well-being (Short Warwick-Edinburgh Mental Well-being Scale), resilience (10-item Connor-Davidson Resilience Scale), self-compassion (Self-Compassion Scale), motivational climate (Motivational Climate for Youth Sports Questionnaire), and psychological skills (Athletic Coping Skills Inventory-28) at three time points, approximately three months apart. Baseline models indicate variability in resilience and well-being over time. Predictive models suggest self-compassion (p=.001) and psychological skills (p=.001) at Time 1 predicted a (positive) rate of change in resilience over the three time points. Self-compassion (p=.008) and psychological skill (p=.008) at Time 1 also predicted a (positive) rate of change in well-being over the same period of time. Fit indices were acceptable for all models (CFI >.95, TLI >.95, RMSEA
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".