VOICE: Exploring the Experiences of University Students who have Experienced Gender-Based Violence
Bibliographic record
Abstract
Introduction: The occurrence and severity of gender-based violence (GBV) on Canadian higher education campuses has been a concern for decades. In September 2021, there were multiple reports of GBV at several Canadian universities with Western University being an exceptional case. The goal of this study was to explore: (1) the experiences of students who have experienced GBV during their enrollment at Western University; (2) the impact of GBV on students’ academics; and (3) student knowledge and experience with GBV resources.\nMethods: This cross-sectional, qualitative study used an interpretive description framework and dialogue maps, underpinned by intersectionality. Seventeen undergraduate students were interviewed using a semi-structured interview guide.\nResults: Undergraduate students struggled to label their experiences as GBV and were unable to avoid contact with their perpetrator, as they both were required on campus. Students identified both short-term and long-term academic consequences of GBV including falling behind academically and needing to rethink future academic goals. Students reported engaging with formal and informal resources following their GBV experience with the most common barriers being related to accessibility and facilitators being related to acceptability of the resource.\nConclusion: Students face unique challenges when experiences of GBV occur in the University setting, particularly related to academics and knowledge of available resources. It is important that GBV services and Universities ensure resources are visible, accessible, and acceptable to students.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".