Systematic Review of TeammateBullying and Hazing withRecommendations to Advance PeerAggression Research in Sport
Bibliographic record
Abstract
Although sport participation can have positive benefits for athletes, there are also unique risks for receipt of aggression. Bullying and hazing occurs across teammates in this setting and can have detrimental physical and psychological effects on athletes. This systematic review was designed to explore the sport literature to (1) uncover conceptualizations and associated prevalence of bullying and hazing and (2) elucidate factors related to bullying and hazing. PRISMA guidelines were followed when completing the review, and 38 studies were included. Prevalence rates vary from less than 10% to over 70% across studies, and most studies sampled athletes from the United States or Canada. Factors associated with bullying or hazing experiences included younger athlete ages, newcomer status on team, increased Machiavellianism, aggressive-coercive personality styles, male gender, lower ability or skill level, and social norms for aggression. To strengthen future research in this area, scholars should more intentionally follow American Psychological Association’s Journal Article Reporting Standards and should ensure that chosen definitions and measures of bullying and hazing are reliable and valid in selected samples. Furthermore, it is impossible to consistently understand prevention and intervention efficacies related to bullying and hazing until researchers can define and delineate between varied forms of teammate-to-teammate aggression.
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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 | 0.025 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".