Mindful self-reflection as a strategy to support sustainable high-performance coaching: A mixed method study
Bibliographic record
Abstract
High-performance coaches (HPCs) encounter a wide range of demands and face challenges engaging in self-care and recovery activities to promote a sustainable coaching career. In this innovative mixed method study, we aimed to gain an understanding of HPCs’ experiences of a brief mindful self-reflection intervention. To address this aim, 18 HPCs completed an 8-week daily intervention and reported their ratings of mood and energy via daily SMS-diaries. Self-reported measures of self-compassion, mindfulness, and well-being were collected at pre-intervention, 2 weeks post-intervention, and 6 months post-intervention. Qualitative data consisted of focus group interviews that were conducted 2 weeks after the intervention ended and a written follow-up containing three questions 6 months after the intervention. To further explore the mechanisms throughout the intervention, participants were categorized into two groups, High Mood and Energy (HME) and Low Mood and Energy (LME), and we compared these groupings with qualitative data. Using reflexive thematic analysis, we subsequently developed two overarching themes from these data that characterized group differences, (viz. HME: “Self-aware and open to attend to self-care needs and well-being” and LME: “Reflecting resistance to the intervention and low self-awareness”). Taken together, we interpret these data to suggest this mindful self-reflection intervention has the potential to enhance HPCs’ self-compassion. This work provides knowledge that can help guide both coaches and organizations in their quest to promote sustainable coaching careers in the elite sport context and we offer recommendations for practitioners working with HPCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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; both teacher heads agree on what is shown here.
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".