Resilience, well-being, depression symptoms and concussion levels in equestrian athletes
Bibliographic record
Abstract
Purpose This paper aims to conduct the first cross-sectional survey on depression, Resilience, well-being, depression symptoms and concussion levels in equestrian athletes and to assess whether past concussion rates were associated with depression, resilience and well-being. Design/methodology/approach In total, 511 participants from Canada, Republic of Ireland, UK, Australia and USA took part in an international cross-sectional, online survey evaluating concussion history, depression symptoms, resilience and well-being. Findings In total, 27.1% of athletes met clinically relevant symptoms of major depressive disorder. Significant differences were shown in the well-being and resilience scores between countries. Significant relationships were observed between reported history of concussion and both high depression scores and low well-being scores. Practical implications Findings highlight the need for mental health promotion and support in equestrian sport. Social implications Results support previous research suggesting a need for enhanced mental health support for equestrians. There is reason to believe that mental illness could still be present in riders with normal levels of resilience and well-being. Originality/value This study examined an understudied athlete group: equestrian athletes and presents important findings with implications for the physical and mental health of this population.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".