Evaluation of the Transfer of Training for a Sexual Assault Resistance Program Enhanced with Sexuality Education
Bibliographic record
Abstract
Abstract Introduction The Enhanced Assess , Acknowledge , Act ( EAAA ) Sexual Assault (SA) Resistance Program is a theoretically sound, evidence-based program providing SA resistance education within a positive sexuality framework. It was shown to substantially reduce sexual assault victimization among university women who participate (Senn et al. in New England Journal of Medicine 372 (24), 2326-2335, 2015). Staff training can either enhance or impede successful program scale-up and implementation. In this paper, we evaluate the transfer of training to implementation sites (i.e., postsecondary institutions) using a train-the-trainer model. Methods Using pre- and post-training surveys and post-training interviews conducted from 2016 to 2020 with 33 implementation staff members from multiple sites, we answered the following research questions: 1. Did the training meet its overall goal of preparing implementation staff? 2. What training components were perceived to contribute to training effectiveness and implementation staff preparedness? Results Results suggested that our model of training was effective. Competence, confidence, and knowledge and ability increased significantly after training, and most staff perceived the training to be highly useful and effective (especially for preparing them to address EAAA participant issues). Practice and feedback from trainers through active learning techniques were especially important. Although implementation staff reported being well prepared to deliver the training or program, they reported being less prepared for handling other implementation-related activities and issues (that the training was not necessarily designed to address in-depth). Conclusions Our findings suggest a need to enhance existing training on self-care and supporting program facilitators and for ongoing support and reminders from program purveyors to ensure that implementers are making use of existing resources. This study fills important gaps in the literature as few studies have examined the transfer of training for SA prevention programming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".