Exploring Mindfulness and Self-Compassion as Mental Health Resources for High-Performance Coaches in Canada
Bibliographic record
Abstract
High-performance coaches, much like the athletes they work with, are performers who face significant stressors that challenge their mental health. This study explores mindfulness and self-compassion as potential resources to support coaches in managing these demands. Using a convergent mixed-methods design, qualitative discussions with six high-performance coaches revealed contextual demands and varied perceptions of these resources. Quantitative analysis of survey responses from 78 high-performance coaches demonstrated that self-compassion uniquely predicted mental ill-health (i.e., burnout) beyond mindfulness (Δ R2 = .10, p < .001), while mindfulness uniquely predicted mental well-being (i.e., thriving) beyond self-compassion (Δ R2 = .09, p = .003). These findings suggest that mindfulness and self-compassion may play complementary roles in supporting both positive and negative aspects of mental health. This research highlights the potential value of incorporating these resources into mental health programs for high-performance coaches and encourages further exploration of their application in elite sport settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".