Can Learning Self-Regulatory Competencies Through a Guided Intervention Improve Coaches’ Burnout Symptoms and Well-Being?
Bibliographic record
Abstract
While coaches are considered at risk of experiencing burnout, there is an absence of intervention studies addressing this syndrome. The purpose of this qualitative study was to conduct a self-regulation intervention with five Canadian developmental (n = 2) and elite (n = 3) sport coaches (three men, two women) experiencing moderate to high levels of burnout and examine the perceived impact of this intervention on their self-regulation capacity and experiences of burnout and well-being. The content analysis of the coaches’ outtake interviews and five bi-weekly journals revealed that all five of them learned to self-regulate more effectively by developing various competencies (e.g., strategic planning for their well-being, self-monitoring) and strategies (e.g., task delegation, facilitative self-talk). Four of the coaches also perceived improvements in their symptoms of burnout and well-being. Sport psychology interventions individualized for coaches are a promising means for helping them manage burnout and enhance their overall functioning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".