“Hardly Able to Move, Much Less Open a Book”: A Systematic Review of the Impact of Sexual and Gender-Based Violence Victimization on Educational Trajectories
Bibliographic record
Abstract
Sexual and gender-based violence (SGBV) is a highly prevalent issue, both in North America and globally, with well-recognized adverse impact on survivors’ physical, emotional, and economic well-being. The objective of this systematic review is to collect and synthesize empirical work on the effects of SGBV victimization on educational trajectories, goals, attainment, and outcomes. The review summarizes what is known about factors associated with victimization that affect survivors’ educational trajectories and highlights gaps in the literature pertaining to the effects of victimization on education. Five databases were searched for this review: Web of Science, Sociological Abstracts, PubMed, APA PsycInfo, and ERIC. For inclusion, the articles must present research on the academic impact of any form of SGBV experienced in higher education and must have been conducted in the United States or Canada. The 68 studies that met these criteria presented research on six key areas of educational outcomes: impacts on academic performance and motivation; attendance, dropout, and avoidance; changes in major/field of study; academic disengagement; educational attitudes and satisfaction; and academic climate and institutional relationships. Research also revealed factors mediating the relationship between SGBV exposure and educational outcomes such as mental health, physical health, social support, socioeconomic status, and resiliency, which we summarize in a pathway model. The research reviewed had significant limitations, including weak study designs, limited generalizability, and diversity concerns. We offer recommendations for future research on this topic.
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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 | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".