A Technology-Based Training Tool for a Health Promotion and Sex Education Program for Justice-Involved Youth: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Justice-involved youth are especially vulnerable to mental health distress, substance misuse, and risky sexual activity, amplifying the need for evidence-based programs (EBPs). Yet, uptake of EBPs in the justice system is challenging because staff training is costly in time and effort. Hence, justice-involved youth experience increasing health disparities despite the availability of EBPs. OBJECTIVE: To counter these challenges, this study develops and pilot-tests a prototype of a technology-based training tool that teaches juvenile justice staff to deliver a uniquely tailored EBP for justice-involved youth-PHAT (Preventing HIV/AIDS Among Teens) Life. PHAT Life is a comprehensive sex education, mental health, and substance use EBP collaboratively designed and tested with guidance from key stakeholders and community members. The training tool addresses implementation barriers that impede uptake and sustainment of EBPs, including staff training and support and implementation costs. METHODS: Staff (n=11) from two juvenile justice settings pilot-tested the technology-based training tool, which included five modules. Participants completed measures of HIV and sexually transmitted infection (STI) knowledge, sex education confidence, and implementation outcomes such as training satisfaction, adoption, implementation, acceptability, appropriateness, and sustainability. PHAT Life trainers assessed fidelity through two activity role plays participants submitted upon completing the training modules. RESULTS: =3.07; P=.01), and were very satisfied (mean 4.42, SD 0.36) with the training tool and the PHAT Life curriculum. They believed that the training tool and curriculum could be adopted, implemented, and sustained within their settings as an appropriate and acceptable intervention and training. CONCLUSIONS: Overall, the results from this pilot test demonstrate feasibility and support continuing efforts toward completing the training tool and evaluating it within a fully powered randomized controlled trial. Ultimately, this study will provide a scalable option for disseminating an EBP and offers a more cost-effective and sustainable way to train staff in an EBP.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".