The use of technology and mobile health apps in child maltreatment interventions: Perspectives of TF-CBT therapists and SafeCare providers
Bibliographic record
Abstract
Previous research has demonstrated positive effects of using technology as an augmentation to evidence-based youth mental health treatment and parenting programs, however, limited research has examined the use of technology in evidence-based child maltreatment (CM) prevention and treatment. The purpose of this paper is twofold: 1) to understand the application of technology and mobile health apps in CM prevention and treatment programs and 2) examine the acceptability of CM programs with an evidence-informed app, JoyPop™, developed to promote resilience through emotion regulation. Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) therapists (N = 12) and SafeCare© providers (N = 12) were recruited and participated in virtual semi-structured interviews. Participants tested the JoyPop™ app over one week and were interviewed to assess their perspectives about and experiences using technology and mobile health apps in practice (aim 1) and the potential use of JoyPop™ as a program augmentation (aim 2). Data collection and analysis was guided by qualitative descriptive analysis. Participants reported that technology and apps supporting positive coping strategies, including JoyPop™, are relevant to the goals of CM programs and can be used enhance client engagement and skill uptake. Participants noted that JoyPop™'s features more strongly align with the components of TF-CBT than Safecare. Limitations of technology use in practice are discussed. Supplementing CM programs with technology and mobile health apps may be used to bolster client engagement and positive outcomes. Future research is needed to address the efficacy and client acceptance of technology and app usage in practice. • Technology and mobile health apps are a promising supplementation to support CM prevention and treatment program goals for parents and youth. • Providers of evidence-based programs support the use of technology and mobile health apps to increase client engagement and reduce caregiver stress. • The JoyPop™ app, specifically, offers features that align well with programmatic components of the evidence-based intervention Trauma Focused-Cognitive Behavioral Therapy. • Future research with program consumers is warranted to guide the successful integration of tecnology in CM interventions.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".