Family-Focused Digital Mental Health Care for Oppositional Symptoms: A Retrospective Analysis of Pediatric and Caregiver Outcomes (Preprint)
Bibliographic record
Abstract
Abstract Background Oppositional symptoms in youth are characterized by an angry or irritable mood and excessive defiance (eg, arguing), negatively impacting the mental well-being of children, adolescents, and their caregivers. Pediatric digital mental health interventions (DMHIs) that approach care from a whole-family perspective may effectively address mental health (MH) symptoms in both pediatric participants and their caregivers, though this has not been explored in the context of oppositional symptoms. Objective The purpose of this study was to assess oppositional symptoms in children and adolescents (aged 6 to 17 years) participating in care within the real-world conditions of a family-centered DMHI. We aimed to (1) examine baseline oppositional severity and its associations with child demographic and clinical characteristics (eg, co-occurring MH symptoms), and caregiver symptoms; (2) evaluate demographic, clinical, and engagement factors associated with oppositional symptoms during care with the DMHI; and (3) determine whether changes in oppositional symptoms during care are associated with improvements in caregivers’ stress, burnout, and sleep. Methods Retrospective analyses included 3781 child-caregiver pairs who participated in coaching and therapy with Bend Health Inc, a family-centered, pediatric DMHI. Assessments at baseline and monthly during care measured pediatric and caregiver symptoms. Children and adolescents were grouped by oppositional severity at baseline: not significant, subclinical, and clinical. Pediatric characteristics, care type, and caregiver symptoms were compared between groups. Linear mixed-effects models assessed oppositional symptoms over months and then tested whether oppositional severity and rate of symptom improvement were associated with caregiver outcomes over time. Results Baseline oppositional symptoms were not significant for 51.55% (1949/3781), subclinical for 26.47% (1001/3781), and clinical for 21.98% (831/3781). More severe oppositional symptoms were associated with younger age ( P <.001), nonfemale sex ( P <.001), White race or ethnicity ( P <.001), higher rates of MH diagnoses (all P <.001), and higher rates of co-occurring inattention, hyperactivity, depression, and sleep problems (all P <.001). Odds of elevated caregiver symptoms increased with more severe oppositional symptoms (all P <.001). At the end of care (final follow-up), oppositional symptoms improved for 73.93% (740/1001) with subclinical symptoms and 82.43% (685/831) with clinical symptoms. Symptom trajectories followed a logarithmic curve, with the greatest improvements in the first several months ( P <.001). While more severe oppositional symptoms were associated with more severe caregiver stress, burnout, and sleep problems (all P <.001), monthly improvements in caregiver symptoms were significantly larger for those whose child improved more quickly (all P <.001). Conclusions Family-centered DMHIs may effectively address pediatric oppositional symptoms, as well as co-occurring impairments in caregiver well-being. These findings highlight the broader, system-level impact of scalable DMHIs (such as Bend) in addressing complex family MH needs. Future work should examine these effects in the long term and evaluate opposition-specific care pathways within DMHIs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".