Clinical and Psychosocial Outcomes Associated With a Tele-behavioral Health Platform for Families: Retrospective Study
Bibliographic record
Abstract
BACKGROUND: The burden of pediatric mental illness in the United States has steadily worsened over the past decade. A recent increase in employer-sponsored behavioral health programs has focused on the needs of the general population. However, these programs do not provide the specialty mental health care required for children, adolescents, and their families. OBJECTIVE: This study aimed to evaluate the effects of a technology-enabled pediatric and family behavioral health service on clinical outcomes among children and caregiver strain. The service is available to commercially insured populations and provides educational content; tele-behavioral health care, including coaching, therapy, and psychiatry; and care escalation and coordination. METHODS: A retrospective cohort analysis of members using the service between February and September 2022 was conducted. Clinical outcomes for children and their caregivers were collected using the Pediatric Symptom Checklist-17 (PSC-17), Generalized Anxiety Disorder 7-item (GAD-7), Patient Health Questionnaire 8-item (PHQ-8), and Caregiver Strain Questionnaire-Short Form 7 (CGSQ-SF7). Rates of reliable improvement were determined by calculating the reliable change index for each outcome. Paired, 2-tailed t tests were used to evaluate significant changes in assessment scores at follow-up compared to baseline. RESULTS: Of the 4139 participants who enrolled with the service, 48 (1.2%) were referred out for more intensive care, 2393 (57.8%) were referred to coaching, and 1698 (41%) were referred to therapy and psychiatry. Among the 703 members who completed the intervention and provided pre- and postintervention outcomes data, 386 (54.9%) used psychoeducational content, 345 (49.1%) received coaching, and 358 (50.9%) received therapy and psychiatry. In coaching, 75% (183/244) of participants showed reliable improvement on the PSC-17 total score, 72.5% (177/244) on the PSC-17 internalizing score, and 31.5% (105/333) on the CGSQ-SF7 total score (average improvement: PSC-17 total score, 3.37 points; P<.001; PSC-17 internalizing score, 1.58 points; P<.001; and CGSQ-SF7 total score, 1.02 points; P<.001). In therapy and psychiatry, 68.8% (232/337) of participants showed reliable improvement on the PSC-17 total score, 70.6% (238/337) on the PSC-17 internalizing score, 65.2% (219/336) on the CGSQ-SF7 total score, 70.7% (82/116) on the GAD-7 score, and 67.5% (77/114) on the PHQ-8 score (average improvement: PSC-17 total score, 3.16 points; P<.001; PSC-17 internalizing score, 1.66 points; P<.001; CGSQ-SF7 total score, 1.06 points; P<.001; GAD-7 score, 3.00 points; P<.001; and PHQ-8 score, 2.91 points; P<.001). CONCLUSIONS: Tele-behavioral health offerings can be effective in improving caregiver strain and psychosocial functioning and depression and anxiety symptoms in a pediatric population. Moreover, these digital mental health offerings may provide a scalable solution to children and their families who lack access to essential pediatric mental health services.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".