Implementing a scoping review to explore sport officials' mental health
Bibliographic record
Abstract
Introduction: Sport officials are tasked with applying rules, maintaining fairness, and ensuring athlete safety. However, sport officials experience anxiety, burnout, and non-accidental violence, with the incidence of these events increasing worldwide. This has led to rising attrition rates among sport officials, with many sport organizations concerned for their operational capacity. The effects of anxiety, burnout, and non-accidental violence might contribute to or be indicative of sport officials' negative mental health outcomes. To develop a clear understanding of how sport officials' mental health is affected by their occupation, it is necessary to identify the mental health outcomes and predictors they experience, and to what extent. The purpose of this scoping review was to identify and examine the empirical research and policy documents surrounding sport officials' mental health. Method: One thousand, two hundred six articles were identified across four databases: PubMed, Web of Science, SportDiscus, and PsycINFO. Next, a policy search was conducted on the respective international governing body websites from English-speaking countries for the 60 included sports. Following screening, 18 studies and one policy document met the inclusion criteria for exploring sport officials' mental health. Results: = 1). The research demonstrated that sport officials frequently experienced negative mental health outcomes and predictors including anxiety, depression, burnout, lower mental health literacy, and high levels of stigmatization towards mental health. Discussion: These outcomes were influenced by gender/sex, age, and experience. There is a need to explore personal and environmental (including occupational) factors that cause or contribute to sport officials' mental health symptoms and disorders.
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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 | 0.082 | 0.233 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.048 | 0.034 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".