Challenges Experienced by Women High-Performance Coaches: Sustainability in the Profession
Bibliographic record
Abstract
High-performance (HP) coaching is a demanding profession. The proportion of woman HP coaches is reported to be in the range of 8.4–20%. Mental health concerns in elite sports have recently gained attention, but mainly focusing on athletes. Beyond coach burnout, limited attention has been given to coaches’ mental health. A recent coach burnout review included only one paper that focused exclusively on women. It has been argued that women HP coaches face greater challenges in a male-dominated coaching culture. The purpose of this study was to explore challenges experienced by women HP coaches and their perceived associations with sustainability and mental health. Thirty-seven female HP coaches participated by answering a semistructured, open-ended questionnaire. All responses were analyzed using inductive thematic analysis, which resulted in two general dimensions: challenges of working as women HP coaches and sustainability and well-being as women HP coaches. Overall, results indicate that challenges reported might be common not only for all HP coaches, but also highlight gender-specific elements. Consequently, coach retention and sustainability would benefit from more attention on well-being and mental health among HP coaches.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".