Sexual Assault as a Contributor to Academic Outcomes in University: A Systematic Review
Bibliographic record
Abstract
Sexual assault continues to be a prevalent and consequential experience for university students. The aim of this systematic review was to synthesize the literature on the academic consequences of the sexual assault for university students. There is currently no comprehensive review of the literature focusing on the academic consequences for university students who experienced sexual assault. This review was conducted based on searches from five databases including Academic Search Complete, Education Search Complete, Education Resources Information Center (ERIC), PsycINFO, and Google Scholar. We identified 13 articles that examined academic consequences of sexual assault during university. Across all studies, sexual assault was associated with more academic problems including lower grade point average, dropping out of university, and self-regulated learning problems. Although the number of articles is small, the results are consistent. Practically, this means that universities, those providing psychological services, and victims themselves need to understand that the consequences are not just physical and psychological but can also negatively impact academic achievement. Our review also identifies limitations in the literature regarding this topic such as methodological concerns, diversity and inclusion concerns, and the need for future work to investigate mediators of the relationship between sexual assault and academic outcomes. We offer recommendations for future research to combat the concerns identified. Development of interventions to support those who experience sexual assault during university necessitates overcoming the limitations identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".