Symposium - Mental Health in Elite Sport: Applied Perspectives from Across the Globe
Bibliographic record
Abstract
Despite the potential positive influence of sport, we have seen in recent years several examples of elite athletes experiencing mental health (MH) problems, which corresponds with research findings demonstrating significant levels of mental ill-health among some athlete populations. How can we best understand this? Is a downside to the intense involvement in organized sport needed for athletes to be successful? It is our collective view that the relative failure to address MH in sport over the years has been in part due to prevailing stigma and false misconceptions that athletes have an indestructible psyche. However, in today’s society, gaps in adequate MH support are more due to a lack of knowledge, skills, and funding within organizations. This symposium is a showcasing four chapter from the International Perspectives on Key Issues in Sport and Exercise Psychology on “Mental Health in Elite Sport: Applied Perspectives from Across the Globe” published in partnership with the International Society of Sport Psychology. First, we will set the scene for understanding and situating mental health in elite sport. Second, Johanna Belz and colleagues will outline examples of good practice in preventing mental disorders and promoting mental health in elite athletes in Germany. Third, Frank Lu will describe a Taiwanese case of an archery coach as an example to report how the coach’s leadership influence athletes’ MH, performance, and satisfaction. Fourth, Franco Noce will illustrate the organization of sport psychology and MH problems in Brazil. Fifth, Natalie Durand-Bush outlines the context of MH in Canadian high-performance sport, a review of relevant MH support pathways and organizations and the forthcoming Canadian national MH strategy. Finally, this tour around the world ends in a discussion led by Kristoffer Henriksen, who will illustrate the global differences in MH service provision within different contexts, and organizations in the form of five postulates.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".