Reducing Alcohol and Opioid Use Among Youth in Rural Counties: An Innovative Training Protocol for Primary Health Care Providers and School Personnel
Bibliographic record
Abstract
BACKGROUND: Given that youth alcohol use is more common in rural communities, such communities can play a key role in preventing alcohol use among adolescents. Guidelines recommend primary care providers incorporate screening, brief intervention, and referral to treatment (SBIRT) into routine care. OBJECTIVE: The aim is to train primary care providers and school nurses within a rural 10-county catchment area in Pennsylvania to use SBIRT and facilitate collaboration with community organizations to better coordinate substance use prevention efforts. METHODS: To build capacity to address underage drinking and opioid use among youth aged 9-20 years, this project uses telehealth, specifically Project ECHO (Extension for Community Healthcare Outcomes), to train primary care providers and school nurses to address substance use with SBIRT. Our project will provide 120 primary care providers and allied health professionals as well as 20 school nurses with SBIRT training. Community-based providers will participate in weekly virtual ECHO sessions with a multidisciplinary team from Penn State College of Medicine that will provide SBIRT training and facilitate case discussions among participants. RESULTS: To date, we have launched one SBIRT ECHO project with school personnel, enrolling 34 participants. ECHO participants are from both rural (n=17) and urban (n=17) counties and include school nurses (n=15), school counselors (n=8), teachers (n=5), administrators (n=3), and social workers (n=3). Before the study began, only 2/13 (15.5%) of schools were screening for alcohol use. CONCLUSIONS: This project teaches primary care clinics and schools to use SBIRT to prevent the onset and reduce the progression of substance use disorders, reduce problems associated with substance use disorders, and strengthen communities' prevention capacity. Ours is an innovative model to improve rural adolescent health by reducing alcohol and opioid use. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21015.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".