Beyond Sexual Assault Prevention: Targeted Outcomes for Empowerment Self-Defense
Bibliographic record
Abstract
Empowerment Self-Defense (ESD) has been shown to be effective in reducing risk of sexual assault victimization among women, but because research in this area is still in its infancy, less is known about additional intervention outcomes that may explain how and why the intervention is effective and about other ways that ESD affects students. The purpose of this study was to examine ESD instructor perspectives about intervention outcomes they perceive to be most important for their students. Using qualitative case-study methodology, interviews from 15 ESD instructors from the United States and Canada were conducted and analyzed using thematic analysis, which yielded six themes: Agency, boundaries, core beliefs, health and healing, somatic experiences, and gender and intersectionality, with each theme having two or more subthemes. Although some of these outcomes have been quantitatively evaluated in previous ESD studies, over half ( n = 10) have not yet been empirically measured and are the focus of this article. These 10 outcomes include enactment, self-determination, nonverbal communication, relationship quality, self-worth, healing, physical strength and power, downregulation, support and solidarity, and societal-level changes. In addition to developing standardized tools to measure these outcomes, future research should quantitatively evaluate these outcomes across diverse student populations and explore their effect on producing the profound outcome associated with ESD, which is reduced risk for sexual assault victimization.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".