Mental Health and Officiating Performance
Bibliographic record
Abstract
Instances of abuse are shown to reduce the mental well-being of sports officials (Noel et al., 2022). Moreover, 90% of football referees believe common mental disorders (CMD) can negatively influence refereeing performances, however, only 18% seek medical advice (Gouttebarge et al., 2017). Existing literature has yet to quantify the relationship between performance and mental health in sports officials. Therefore, this study will use a subjective rating of officiating performance “(e.g., positioning, rule application, communication, etc.) developed by Hancock et al., (2022), and measures of mental health outcomes using the Kessler K10 psychological distress scale (Kessler et al., 2002), and the Warwick-Edinburgh mental well-being scale (WEMWBS; Tennant et al., 2007) to determine if there is a direct relationship between mental health and performance. A sample of 274 referees (85% male, 84% Caucasian, Mage = 47.3, Mexperience = 19.0) from Canada Basketball participated in this study. A simultaneous Multiple Linear Regression predicting performance from psychological distress and well-being was significant F (2, 268) = 29.90, p < .001), accounting for 17% of the variation in officials’ performance. Interestingly, while well-being was a significant predictor of performance (β = 0.45, p < .001), psychological distress was not. These results are contextualized within the Dual Continuum Model of mental health, which suggests that mental health and mental illness are two separate but related constructs, and support well-being-based initiatives within this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".