Sexual harassment, sexual violence and subsequent depression and anxiety symptoms among Swedish university students: a cohort study
Bibliographic record
Abstract
PURPOSE: To determine the gender-specific impact of recent exposure to different forms of sexual harassment and sexual violence (SHV) on depression and anxiety symptoms three, six, and nine months later. METHODS: We recruited 2229 women and 1274 men studying at Swedish universities and followed them with web-surveys every three months over one year. We estimated mean differences (MDs) of depression and anxiety symptoms between exposed and unexposed at each follow-up, adjusting for prior SHV, prior depression and anxiety symptoms and potential confounders. RESULTS: For women, sexual harassment (wide subjective definition) was associated with higher symptom levels of depression (MD 1.0 [95% CI: 0.3; 1.7]) and anxiety (MD 0.8 [95% CI: 0.3; 1.4]) three months later. Unwanted sexual attention was associated with higher symptom levels of anxiety three (MD 0.5 [95% CI: 0.1; 0.8]) and six months later (MD 0.4 [95% CI: 0.0; 0.7]). Exposure to sex against ones will was associated with higher depression symptoms three (MD 1.7 [95% CI: 0.1;3.4]), and six months later (MD 3.1 [95% CI: 1.0; 5.2]). Trends indicated that associations with subsequent mental health differed between forms of SHV among women, and that most associations were more pronounced in temporal proximity to the exposures. For men, we refrain from interpreting the results since they showed high variability and were not robust to sensitivity analyses using multiple imputation to account for missing outcome data. CONCLUSIONS: Among women, several forms of SHV were associated with higher subsequent depression and anxiety symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".