Sport-Related Concussion and Mental Health Outcomes in Elite Athletes: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Elite athletes can experience a diverse range of symptoms following post-concussive injury. The impact of sport-related concussion on specific mental health outcomes is unclear in this population. OBJECTIVE: The aim was to appraise the evidence base regarding the association between sport-related concussion and mental health outcomes in athletes competing at elite and professional levels. METHODS: A systematic search of PubMed, EMBASE, SPORTDiscus, PsycINFO, Cochrane, and Cinahl databases was conducted. RESULTS: A total of 27 studies met inclusion criteria for review. Most of the included studies (67%, n = 18) were published in 2014 or later. Study methodology and reporting varied markedly. The extant research has been conducted predominantly in North America (USA, n = 23 studies; Canada, n = 3), often in male only (44.4%, n = 12) and college (70.4%, n = 19) samples. Depression is the most commonly studied mental health outcome (70.4%, n = 19 studies). Cross-sectional retrospective studies and studies including a control comparison tend to support an association between concussion exposure and depression symptoms, although several studies report that these symptoms resolved in the medium term (i.e. 1 month) post-concussion. Evidence for anxiety is mixed. There are insufficient studies to draw conclusions for other mental health domains. CONCLUSION: Consistent with current recommendations to assess mood disturbance in post-concussive examinations, current evidence suggests a link between sports-related concussion and depression symptoms in elite athletes. Causation cannot be determined at this stage of enquiry because of the lack of well-designed, prospective studies. More research is required that considers a range of mental health outcomes in diverse samples of elite athletes/sports.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".