Family-Focused Digital Mental Health Care for Oppositional Symptoms: A Retrospective Analysis of Pediatric and Caregiver Outcomes (Preprint)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».