Family-based substance use screening and intervention for adolescents with chronic medical conditions: a study protocol to implement SBIRT-family within school-based health centers
Bibliographic record
Abstract
Background: Adolescents with a chronic medical condition (CMC) have an increased risk of developing a substance use (SU) disorder, despite the impact that SU may have on disease-related outcomes. School-based health centers (SBHCs) offer universal screening, brief intervention, and referral for adolescents with chronic medical conditions for substance use treatment. Screening, Brief Intervention, and Referral to Treatment (SBIRT) is an evidence-based early intervention used to detect and address risky substance use that has yet to be broadly adopted in public schools. Moreover, despite extensive research supporting caregiver involvement in treatment for adolescent substance use, SBIRT models that actively engage caregivers are lacking. The primary goal of this qualitative study is the identification of contextual determinants (e.g., barriers and facilitators) of SBHCs implementation potential and adaptation needs of a family-based SBIRT protocol for integration into SBHCs. Methods: We are conducting this study in two SBHCs within the Chicago Public School system. In these SBHCS we are conducting focus groups with school partners (∼ 30 SBHC staff,∼25 adolescents with chronic medical conditions, and∼25 caregivers). Focus groups will be audio recorded and conducted in English. The semi-structured focus group guides were designed based on the Health Equity Implementation Framework (HEIF) and the Consolidated Framework for Implementation Research (CFIR). We will develop a codebook based on emerging codes from the transcripts and constructs from HEIF and CFIR. Emerging themes will be summarized highlighting similarities and differences between and within the different groups and SBHCs. Descriptive statistics and chi-square tests of associations will be used to assess the distribution of responses on the assessments between the different sites. Discussion: This study will describe key implementation determinants and SBIRT-Family adaptation needs from the perspective of multiple end-users. Results will provide insights for a randomized pilot hybrid type 2 effectiveness implementation study of the adapted SBIRT-Family model in two SBHCs assessing effectiveness outcomes (SU and linkage to treatment) and implementation outcomes (reach, adoption, equity, and cost). This research protocol will provide formative data to inform the development of a highly scalable approach that can be used in SBHCs across the country to serve a vulnerable population of adolescents with chronic medical conditions.
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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 | 0.042 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".