Refining a Video and Text Message Intervention (STAR, Skills Training in Active Recovery) to Prevent the Onset or Escalation of Posttraumatic Stress and Opioid Misuse Among Recent Sexual Assault Survivors: Community Engaged Study
Bibliographic record
Abstract
Background: Sexual violence is prevalent, and the consequences can be chronic and impairing. However, few interventions exist to prevent the onset or escalation of posttraumatic stress symptoms and opioid misuse among recent sexual violence survivors. Objective: This study describes a collaborative process of updating an integrated postsexual assault video and developing an SMS text messaging intervention program with a community advisory board (CAB) of sexual assault survivors. Methods: Research team members met virtually for six 60-90-minute meetings with a 5-member CAB of sexual assault survivors with diverse racial and gender identities located throughout the United States. CAB members provided feedback on written documents detailing an adapted video script and newly developed text intervention to address the risk of substance misuse and posttraumatic stress disorder symptoms following sexual assault. CAB members also received SMS text messages to provide feedback from the end-user perspective. Results: We identified overarching themes to improve relatability (destigmatize and increase awareness of support, reduce technical language, and increase representation in actors), content (increase social support, include substance-related assault, and suggest activities), and wording (normalize different terms for sexual assault and reduce insensitive language) for the video intervention. For the text intervention, we identified themes relating to acceptability (timing, frequency, and format of texts), relatability (having an avatar introduce the program and identifying the study name in messages), content (messaging), and wording (increasing clarity). Conclusions: Findings reinforce the importance of including community members' perspectives and suggestions to improve the acceptability and relatability of interventions, including the video and SMS text message intervention described here.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".