Digital Mental Health Screening, Feedback, and Referral System for Teens With Socially Complex Needs: Protocol for a Randomized Controlled Trial Integrating the Teen Assess, Check, and Heal System into Pediatric Primary Care
Bibliographic record
Abstract
BACKGROUND: Teens with socially complex needs-those who face multiple and potentially overlapping adversities-are disproportionately affected by several barriers to mental health screening and treatment. Pediatric primary care (PPC) is a typically low-stigmatized setting for teens that is visited at least annually. As such, implementing digital mental health tools (DMH), as low-intensity treatments in PPCs may increase the reach of such tools for teens with socially complex needs. OBJECTIVE: This study aimed to evaluate the Teen Assess, Check, and Heal (TeACH) System in comparison to a control condition while integrated into PPCs at 2 Medical Centers serving teen patients in Chicago, Illinois. Through collaboration with key players throughout the design and implementation planning phases, the TeACH System is hypothesized to increase teen patient self-reported engagement with DMH and address specific individual-level barriers to mental health care, compared with a digital psychoeducation control condition. METHODS: Eligible participants will be recruited through PPC clinics housed within the University of Illinois Chicago (UIC) and Rush University Medical Center (RUSH). Recruitment involves invitations from research staff members and primary care clinicians and staff members, as well as posting flyers with QR codes at the specified clinics. All participants complete a brief demographic survey, baseline survey, and Kiddie-Computerized Adaptive Tests Anxiety Module. Participants are randomized to receive either the control condition (digital evidence-based workbook) or the intervention (TeACH System Feedback and Resources). All randomized participants will then be invited to complete an immediate and 1-week follow-up survey. The primary outcomes assess changes in engagement with DMH (ie, likelihood to use DMH for anxiety and actual DMH use) and individual-level barriers to mental health care (ie, symptom understanding and confidence to act). Descriptive analyses will be conducted to characterize the sample and usability ratings of the TeACH System. Linear or generalized linear mixed effects regression models will examine differences in primary outcomes over time. RESULTS: Recruitment began in July 2024 and data collection is expected to be completed by August 2025. To date, 122 teens have assented to complete study activities, 80 have been randomized (an additional 24 teens have had subthreshold anxiety symptoms and were therefore not randomized), and 42 teens have completed the 1-week follow-up assessment. CONCLUSIONS: This study will provide preliminary feasibility data that may inform how the TeACH System and other DMH low-intensity treatments might better engage and support teens with socially complex needs. TRIAL REGISTRATION: ClinicalTrials.gov NCT05466929; https://clinicaltrials.gov/study/NCT05466929. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65245.
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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 | 0.029 | 0.025 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.089 | 0.011 |
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".