Understanding Support Providers’ Views of “Helpful” Responses to Sexual Assault Disclosures: The Impacts of Self-Blame and Physical Resistance
Bibliographic record
Abstract
Prior research on the factors associated with various disclosure responses has often been conducted on sexual assault victims and formal support providers, while informal helpers, who are the most common recipients of disclosures, have received far less attention. This experimental study examined potential informal helpers' views of disclosure reactions and their influence on the self-reported likelihoods of engaging in those responses. Undergraduate students at a large Canadian university ( N = 239) received vignettes describing a hypothetical sexual assault disclosure that varied on victim's self-blame and physical resistance, and then rated common disclosure reactions. The results revealed that participants' perceptions of various responses were at odds with victims' experiences, with many negative responses, such as victim blame and egocentrism, viewed as equally or more helpful than positive responses, such as emotional support. Moreover, when the victim blamed herself and did not physically resist, positive responses were seen as less helpful whereas negative responses were seen as more helpful, with some notable gender differences. Regression analyses indicated that the perceived helpfulness of each response was the strongest predictor of the likelihood of providing that response. Practical implications of these findings are discussed.
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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 | 0.005 | 0.023 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".