Implementing a Digital Child Behavioral Health Prevention Program in Faith-Based Settings in Uganda: A Feasibility Study
Bibliographic record
Abstract
Background: The burden of pediatric mental disorders in low-and middle-income countries (LMICs) is tremendous, but solutions for addressing the burden remain limited. Although digital solutions have potential to improve prevention services, such solutions have not been systematically tested in these countries. Objective: This study explores the use of a digital parenting intervention tool designed for pediatric behavioral health, known as the Pediatric-Behavioral Health Digital Tool, in a preventive service model for low resource communities. We study the feasibility of implementing this new digital health service model and preliminary estimate the potential impacts on parenting and child social emotional outcomes when the program is implemented in faith-based organizations in Uganda. The Pediatric-Behavioral Health Digital Tool is a preventive intervention designed to be implemented by trained community-health-workers to facilitate caregivers' access to the preventive mental health service in community for their young children. The tool is based on the screening, brief intervention, and referral to treatment prevention service model for promoting pediatric behavioral and mental health. Methods: The evaluation study was designed using a pre-post assessment design. The content in Pediatric-Behavioral Health Digital Tool was co-designed with local expert and iteratively adapted based on parents and caregivers as well as community-health-workers and experts who were invited to provide their feedback and suggestions for improvements in content, functions, and delivery model through a series of focus groups and workshops. This pilot evaluation focuses on the pre-post changes of the intervention families (91 families) and 10 community-health-workers. Results: We found high acceptability, appropriateness, and usefulness of the program based on the intervention families' community-health-workers' report. Intervention parents felt safe in using the digital toolkit. Parents felt comfortable for the CHWs asked them personal questions. In estimating the impacts, we found some expected findings on parenting and child social emotional health. Specifically, we found intervention parents become more mindful in their parenting (d=1.61, p=.049), and felt more effective in discipline their child's misbehavior (d=1.29, p=.003) after they receive the intervention. For children, we found improvement on children's social emotional outcomes, measured by decreased parent-child conflict (d=-1.08, p=.002) and increased child emotional regulation skills (d=1.0, p=.049) after their parents receive the intervention. Conclusions: Our Pediatric-Behavioral Health Digital Tool has potential to provide a cost-effective service solution to provide preventive mental health care in communities to promote child social-emotional and mental wellbeing in low-resource settings.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".