The impact of abuse experiences and coping responses on distress and mental health outcomes in sports officials
Bibliographic record
Abstract
Sports officials (e.g., referees) experience multiple role-related stressors, including episodes of verbal, physical, and social media abuse. However, the impact of strategies employed to cope with abuse experiences is unknown. The aims of this study were to: (i) explore the prevalence and frequency of abuse experiences over a single season, (ii) determine the impact of abuse and other stressors on sports officials’ mental health, and (iii) understand the mental health impact of strategies utilised by sports officials to cope with abuse experiences. A total of 303 Gaelic games match officials completed an online survey measuring stressors (including abuse experiences), coping strategies, and mental health outcomes. Correlational and path analyses explored relationships between abuse experiences, coping strategies, distress, and subsequent mental health outcomes. In total, 88.11% of officials reported experiences of verbal abuse, 7.59% physical abuse, and 17.16% social media abuse during the previous season. Greater use of both avoidance-cognitive and approach-oriented coping was associated with higher distress and poorer mental health outcomes following verbal abuse. Moreso, greater use of avoidance-cognitive strategies to cope with verbal abuse from players predicted higher distress which, in turn, predicted higher anxiety, higher depression, and lower mental well-being. For social media abuse, self-blame, planning, and behavioural disengagement were associated with poorer mental health. The findings suggest that sports officials employ maladaptive coping for abuse experiences, negatively impacting on mental health outcomes. Developing alternative strategies, such as mastery-oriented coping, may help sports officials to deal with abuse experiences and better protect their mental health.
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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) | 0.000 | 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 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".