Determinants of anxiety in elite athletes: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective To identify and quantify determinants of anxiety symptoms and disorders experienced by elite athletes. Design Systematic review and meta-analysis. Data sources Five online databases (PubMed, SportDiscus, PsycINFO, Scopus and Cochrane) were searched up to November 2018 to identify eligible citations. Eligibility criteria for selecting studies Articles were included if they were published in English, were quantitative studies and measured a symptom-level anxiety outcome in competing or retired athletes at the professional (including professional youth), Olympic or collegiate/university levels. Results and summary We screened 1163 articles; 61 studies were included in the systematic review and 27 of them were suitable for meta-analysis. Overall risk of bias for included studies was low. Athletes and non-athletes had no differences in anxiety profiles ( d =−0.11, p=0.28). Pooled effect sizes, demonstrating moderate effects, were identified for (1) career dissatisfaction ( d =0.45; higher anxiety in dissatisfied athletes), (2) gender ( d =0.38; higher anxiety in female athletes), (3) age ( d =−0.34; higher anxiety for younger athletes) and (4) musculoskeletal injury ( d =0.31; higher anxiety for injured athletes). A small pooled effect was found for recent adverse life events ( d =0.26)—higher anxiety in athletes who had experienced one or more recent adverse life events. Conclusion Determinants of anxiety in elite populations broadly reflect those experienced by the general population. Clinicians should be aware of these general and athlete-specific determinants of anxiety among elite athletes.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".