The mental health of student-athletes: a systematic scoping review
Bibliographic record
Abstract
The purpose of this study was to provide a systematic scoping review of the research focussing on student-athletes’ mental health (MH). More specifically, we aimed to describe and synthesise: (a) the study and sample characteristics of the dual career (DC) and MH research literature, (b) the types of MH outcomes examined in student-athlete populations, (c) comparisons of student-athletes’ MH in relation to other populations of interest, and (d) the variables associated with student-athletes’ MH. Articles were collected from four databases: SPORTDiscus, PsycInfo, Scopus, and PubMed. In total, 159 studies spanning three decades met the inclusion criteria. Most studies were conducted within the North American collegiate context. The majority (62.5%) examined mental ill-health outcomes (e.g. disordered eating, depression, anxiety), 22.6% examined positive mental health outcomes (e.g. subjective well-being, psychological well-being), and 13.8% combined both perspectives. Most studies using non-student-athlete comparison groups found that student-athletes were at a similar or decreased risk for MH problems, although notable exceptions were identified. Finally, 49 distinct variables were associated with student-athletes’ MH. Most variables related to generic or sport-specific factors, with only a limited number of studies examining DC-specific factors. Findings from our scoping review are critically discussed in view of the existing literature.
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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 | 0.024 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".