Student Experience of School Screening, Brief Intervention, and Referral to Treatment
Bibliographic record
Abstract
BACKGROUND: Screening, Brief Intervention and Referral to Treatment (SBIRT) is a clinical guideline that can help delay, prevent or reduce substance use behaviors in youth. We aimed to describe the experiences of middle and high school (MS and HS) students attending a school with an SBIRT program. METHODS: This was a survey study conducted in 2 school districts that implemented SBIRT programs prior to statewide roll-out of mandatory school SBIRT in Massachusetts, in which students were asked about past-year substance use and then received brief counseling by a school professional. Students in grades that received SBIRT were subsequently invited to complete an electronic questionnaire about their SBIRT experience. RESULTS: A total of 890 students were included in the study (63.7% MS, 36.3% HS). Experiences of school SBIRT were predominantly positive: 74.0% of participants reported that the information received was useful. Students who reported having used substances were less likely to agree that "schools should screen for substance use" than students who did not report substance use (AOR: 0.39, 95%CI: 0.29-0.53). CONCLUSIONS: Most respondents found SBIRT of value, though students with past-year substance use were less positive about the experience. More research is needed to optimize SBIRT delivery in schools.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".