Evaluating the Reach, Usage, Human Support Needs, and Clinical Outcomes of Digital Parent Training for Child Oppositional Defiant Disorder Before and During Wartime: Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: Digital parent training programs (DPTs) have emerged as a scalable solution for treating childhood oppositional defiant disorder (ODD), offering remote access and reduced barriers to care. However, there is limited data on their potential to reach untreated populations and their effectiveness during times of crisis, such as war. OBJECTIVE: This study aimed to evaluate the reach, usage patterns, human support needs, and clinical outcomes of a fully remote guided DPT for child ODD, comparing 2 cohorts treated before and during wartime in Israel. METHODS: Parents of children with ODD were enrolled in a human-supported DPT, with 25 families recruited before and 30 during wartime. Data included self-reported questionnaires (measured before-, postintervention, and 3 months after the end of the intervention), platform usage metrics, and clinician assessments. RESULTS: Most families (62%, 34/55) had not previously received any intervention for their child's behavior problems. Significant self-reported improvements in child behavior (Cohen d≥0.79) and parenting practices (0.39≤Cohen d≤0.87) were found post intervention. On average, families engaged with the program for 138.6 minutes across 31.4 unique logins, supported by 38.8 minutes of human interaction, primarily via messaging. During wartime, parents completed onboarding significantly faster (15.70 days vs 31.36 days) and were more likely to complete the critical "overcoming disobedience" phase (27/30, 90% vs 17/25, 68%). However, while self-reported changes were similar, clinician-rated recovery from ODD was marginally lower during wartime (13/30, 43% vs 17/25, 68%). CONCLUSIONS: DPTs present an acceptable avenue for care that could reach parents who have not sought treatment through traditional channels. However, this study's results suggest that their clinical effectiveness may be lower under extreme stress conditions such as wartime, underscoring the need for future studies in this area.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".