Sexual harassment and abuse; disclosure and awareness of report- and support resources in Norwegian sport- and non-sport high schools: a prospective exploratory study
Bibliographic record
Abstract
Purpose: To examine high school students' disclosure of sexual harassment and abuse (SHA), and awareness of reporting systems and support mechanisms in school among students, leaders, and coaches. Method: = 249) at the participating high schools responded to an adapted version of the questionnaire at T1. Data were analyzed using ANOVA or Welch test, Pearson Chi-Square test, and McNemar test. Results: In total, 11.4 and 34.0% of the adolescents were aware of reporting systems and support mechanisms, respectively, in their schools. Nearly all the leaders, and half of the coaches were aware of these resources. Among the adolescents with lifetime experience of SHA, 20.1% had disclosed their experiences to someone. Girls disclosed more frequently than boys. The elite- and recreational athletes disclosed less often compared with the reference students. A negative change from T1 to T2 was found in disclosure of SHA and awareness of support mechanisms. At T2, 6.5% of the adolescents reported that their school had implemented measures against SHA during the last 12 months. Conclusion: The results emphasize a need for institutional effort to improve information about available report- and support resources and increase the relevance of use of such systems for adolescents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".