The Consequences of Alcohol-Involved Sexual Victimization in Male and Female College Students
Bibliographic record
Abstract
Alcohol intoxication is often involved for both victims and perpetrators of sexual victimization. Yet, alcohol-involved sexual victimization research has mainly focused on female victims, excluding male victims. The current study addresses gaps in the literature by focusing on sex differences in the emotional harms (anxiety and depression symptomatology) experienced by sexual victimization victims when either the perpetrator or victim was drinking. Five-hundred-and-ten undergraduate drinkers (153 male; 357 female) participated. Models included two dichotomized predictors that occurred during participants’ first year of university (sexually victimized when the victim was drinking, sexually victimized by someone who was drinking), and two emotional outcomes (anxiety, depression). Age was controlled in all path analyses and sex was examined as a moderator. When predictors were examined in separate models, both predictors were associated with increased anxiety but not depression. These effects were significantly stronger among men. When both predictors were entered simultaneously, individuals who were victimized by someone drinking displayed increased anxiety, and this relationship was stronger among men than women victims. Being victimized when drinking was no longer associated with anxiety, consistent with prior findings that post-traumatic distress may be minimized when a trauma occurs while the victim is intoxicated. Results highlight the impact sexual victimization can have for both male and female victims, and point to the need for evidence-based policies to prevent emotional second-hand alcohol harms among male and female students alike.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".