Risk factors for sexual harassment and abuse victimization among adolescent athletes and non-athletes: A one-year follow-up study
Bibliographic record
Abstract
BACKGROUND: The association between SHA and negative mental health increases the need to understand risk factors for SHA victimization, which is important for future development of prevention programs. OBJECTIVE: To examine which combinations of demographic- and mental health factors were associated with subsequent SHA victimization, and the prevalence of elite athletes, recreational athletes, and reference students who experienced sexual revictimization. PARTICIPANTS AND SETTING: Norwegian elite athletes and recreational athletes attending sport high schools, and reference students attending non-sport high schools (mean age: 17.1 years) were eligible for participation. METHODS: The participants answered an online questionnaire at two measurement points one year apart, T1 and T2 (n = 1139, 51.1 % girls). After testing for measurement invariance, data were analyzed with Classification and Regression Tree analysis (CRT) using demographic- and mental health variables from T1 as independent variables, and SHA at T2 as outcome. RESULTS: The combination of being a girl with high level of symptoms of eating disorders and other psychological symptoms was associated with subsequent reporting of SHA. Among the students with lifetime experience of SHA at T1 (n = 533, 58.3 %), 49.5 % reported revictimization at T2 (60.9 % girls, 32.2 % boys, p ≤ .001). The prevalence of SHA revictimization was lower among elite athletes (44.3 %) compared with recreational athletes (49.1 %) and reference students (59.4 %, p = .019). CONCLUSION: The combination of female gender and mental health symptoms are risk factors for subsequent SHA victimization. These findings, and the high prevalence of SHA revictimization is important knowledge for developing preventive programs targeting elite athletes, recreational athletes, and reference students.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".