Abuse, Workplace Toxicity, and Referees’ Intentions to Quit: Examining the Roles of Perceived Organizational Support and Resilience
Bibliographic record
Abstract
The growing level of abuse aimed at referees is a major factor driving their intentions to quit the profession. However, only a limited number of studies have empirically examined the mediating and moderating variables that influence the nature and magnitude of this relationship. To address this issue, this study draws from the job demands–resources theory to examine why and under which conditions abuse leads to the intentions to quit of referees. Time-lagged data were collected from 487 amateur-level hockey referees. The results showed that experiences of abuse are positively related to referees’ intentions to quit through perceptions of workplace toxicity. In addition, we found that organizational support and resilience are resources that dampen the relationship between abuse and referees’ intentions to quit. Taken together, these findings offer several important implications for sports organizations and policymakers.
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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 | 0.002 | 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".