An Evaluation of Strategies Used to Maximize Intervention Fidelity in a Randomized Controlled Trial of a Sexual Assault Resistance Program for University Women
Bibliographic record
Abstract
In this paper, we describe and evaluate the strategies used to maximize intervention fidelity in a randomized controlled trial to examine the efficacy of a sexual assault resistance intervention. The EAAA program was based on the best available theory and evidence on how women can successfully resist sexual coercion from male acquaintances. Extensive protocols for hiring, training, and supervising facilitators were established a priori. Detailed intervention manuals were developed that clearly described program goals, learning objectives, core elements, troubleshooting tips, sections that must be delivered verbatim, adaptations that could be made if necessary, and the ideal and minimum dose. Program sessions were audio-recorded, and a subsample of recordings were scored for adherence to the manuals using detailed Intervention Fidelity Checklists (IFC) developed specifically for this research. The Gearing et al. (2011) Comprehensive Intervention Fidelity Guide (CFIG) was employed retrospectively to provide objectivity to our analysis and help identify what we did well and what we could have done better. The SARE (Sexual Assault Resistance Education) Trial received high scores (38 out of 44 (86%) from each of the first two authors on the CFIG, suggesting a high level of intervention fidelity. Although a potential for bias on the part of the two raters was an obvious limitation, as was our neglection to include measures of implementation receipt, which Gearing et al. (2011) recommended, our analysis underscores the utility in employing methods recommended to enhance intervention fidelity when developing and evaluating evidence-based interventions.
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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 | 0.219 | 0.274 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".