The person before the player: is mental health support a missing piece in the eyes of elite athletes?
Bibliographic record
Abstract
The body of research examining athlete mental health continues to grow; however, work exploring the mental health supports available for elite athletes is slower to emerge. The purpose of the present study was to explore, from the athletes’ perspective, (1) how their sport journeys have impacted their mental health, (2) how they have dealt with the stresses and pressures inherent in elite sport, and (3) what supports were/should have been available to support positive mental health within the sport space. Seven semi-structured interviews were conducted with male and female varsity athletes from multiple sports. Interviews covered internal and external pressures, mental health, and formalized support. Reflexive thematic analysis revealed 15 main themes, with 3 of these being further divided into 7 subthemes. All athletes reported experiencing numerous sources of pressure and although their parents and coaches supported them, there was no consistent finding indicating that these significant others provided them with adequate support in response to these pressures. Notably, while all participants conveyed the importance of having access to formalized support for mental health concerns, it was found that most participants had little to no experience with, or access to, professional support for mental health. These findings contribute to the discussion on the importance of athletes’ mental health by highlighting that there is a perceived lack of support and professional resources within the sport environment. Further, it points to a need for implementing formalized support positions for the purpose of helping athletes navigate the pressures inherent to elite sport.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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".