Recognizing and Responding to the Commonly Misunderstood Reactions to Sexual Assault: Evaluation of an Online Curriculum
Bibliographic record
Abstract
Background: Sexual assault remains a serious public health issue with significant impacts on the health and well-being of individual women. Many women's reactions and behaviors post sexual assault are not well understood by the general public, or more worrying, among professionals to whom women frequently turn to for help. An innovative and evidence-informed online curriculum was developed to educate health and social service providers about the range of possible psychological responses and associated behaviors post sexual assault and to better equip them in supporting survivors in their recovery. Methods: The curriculum was evaluated using pre- and post-training tests designed to measure changes in fact-based knowledge, self-assessed knowledge, and procedural knowledge, that is, perceived competency. Results: A total of 759 participants registered to complete the curriculum between July 2018 and July 2019 and 175 completed both the pre- and post-training surveys. Data analyses showed significant improvement in the mean number of correct answers to the fact-based knowledge, self-reflection, and procedural knowledge questions from pre- to post-training. The response to the training was also very positive. Conclusions: The online curriculum was effective in improving participants' knowledge about and response to women who, in the aftermath of a sexual assault, may exhibit reactions or behaviors that are commonly misunderstood.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".