Short-Term Outcomes of a Healthy Relationship Intervention for the Prevention of Sexual Harassment and Sexual Assault in the US Military: Pilot Pretest-Postest Study
Bibliographic record
Abstract
BACKGROUND: Sexual harassment (SH) and sexual assault (SA) are serious public health problems among US service members. Few SH and SA prevention interventions have been developed exclusively for the military. Code of Respect (X-CoRe) is an innovative web-based, multilevel, SA and SH intervention designed exclusively for the active-duty Air Force. The program's goal is to increase Airmen's knowledge and skills to build and maintain respectful relationships, ultimately reducing SH and SA and enhancing Airmen's overall well-being and mission readiness. OBJECTIVE: This pilot study aimed to assess the short-term psychosocial impact (eg, knowledge, attitudes, and self-efficacy) of the web-based component of X-CoRe on a sample of junior enlisted and midlevel Airmen. METHODS: Airmen from a military installation located in the Northeastern United States were recruited to complete the 10 web-based modules in X-CoRe (9/15, 60% male; 7/15, 54% aged 30-35 years). Participants were given pretests and posttests to measure short-term psychosocial outcomes associated with SH and SA. Descriptive statistics and paired 2-tailed t tests were conducted to assess differences from preintervention to postintervention time points. RESULTS: After completing X-CoRe, participants had a significantly greater understanding of active consent (P=.04), confidence in their healthy relationship skills (P=.045), and confidence to intervene as bystanders (P=.01). Although not statistically significant (P>.05), mean scores in attitudes about SH, couple violence, and cyberbullying; perceptions of sexual misconduct as part of military life; and relationship skills self-efficacy with a romantic partner and friend also improved. CONCLUSIONS: The findings from this study demonstrate X-CoRe's effectiveness in improving critical determinants of SH and SA, making it a promising intervention for SH and SA prevention. More rigorous research is needed to determine X-CoRe's impact on SH and SA victimization and the long-term impact on associated psychosocial determinants.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".