Burnout symptoms and recovery processes in eight elite soccer coaches over 10 years
Bibliographic record
Abstract
Elite sport can be stressful, which increases the risk for burnout symptoms to develop, especially when not balanced with sufficient recovery. To study the burnout–recovery process, eight elite soccer coaches were followed for 10 years. All eight were active elite coaches at the inception of this study and reported elevated emotional exhaustion scores on Maslach’s Burnout Inventory Educators Survey (MBI-ES). The coaches completed MBI-ES three additional times (year 3, 7, and 10), and they were also interviewed on the same occasions. At the 3-year follow-up, seven of the eight coaches reduced their exhaustion scores. The coach presenting with unchanged scores both at the 3 - and 7-year follow-up was the only one still coaching at the elite level. All coaches revealed during the interviews that they struggled to manage their work–life balance well; some worked too many hours, some experienced difficulty in managing conflicting role-demands, and some wrestled with external pressures. Their approach to recovery was, however, similar. Apart from moving away from coaching at the elite level, they unanimously mentioned that they changed their approach to coaching to make recovery possible. They achieved the latter by, for example, increasing control and delegating responsibility. According to our longitudinal results, burnout frequently regarded as an end-state can decrease over time, provided that decisive action is taken to change situational factors and personal demands. This frequently meant withdrawing from coaching, which in turn explains why coach retention remains a serious challenge for most organizations with teams/athletes competing at the elite level.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".