Digital Platform for Pediatric Mental Health Support During Armed Conflicts: Development and Usability Study
Bibliographic record
Abstract
Background: The prevalence of mental health disorders among children and adolescents presents a significant public health challenge. Children exposed to armed conflicts are at a particularly high risk of developing mental health problems, necessitating prompt and robust intervention. The acute need for early intervention in these situations is well recognized, as timely support can mitigate long-term negative outcomes. Pediatricians are particularly suited to deliver such interventions due to their role as primary health care providers and their frequent contact with children and families. However, barriers such as limited training and resources often hinder their ability to effectively address these issues. objectives: This study aimed to describe the rapid development of a digital mental health tool for community pediatricians, created in response to the urgent need for accessible resources following the October 7th terror attack in Israel. The goal was to create a comprehensive resource that addresses a wide range of emotional and behavioral challenges in children and adolescents, with a particular focus on those affected by armed conflict and significant trauma exposure. In addition, the study aimed to evaluate the platform's usability and relevance through feedback from primary users, thereby assessing its potential for implementation in pediatric practice. Methods: A digital platform was developed using a collaborative approach that involved pediatricians and mental health professionals from various hospital clinics. The initial framework for the modules was drafted based on key emotional and behavioral issues identified through prior research. Following this, the detailed content of each module was cocreated with input from specialized mental health clinics within the hospital, ensuring comprehensive and practical guidance for community pediatricians. A focus group of 7 primary users, selected for their relevant hospital and community roles, provided feedback on the platform's user experience, content relevance, and layout. The evaluation was conducted using a structured questionnaire complemented by qualitative comments. Results: Fifteen detailed modules were created, each providing information, including anamnesis, initial intervention strategies, parental guidance, and referral options. The focus group feedback demonstrated high satisfaction, indicating a very good user experience (mean 4.57, SD 0.53), content relevance (mean 4.71, SD 0.48), and layout suitability (mean 4.66, SD 0.52). Specific feedback highlighted the value of concise, actionable content and the inclusion of medication information. Participants expressed a strong willingness to regularly use the platform in their practice (mean 4.40, SD 0.53), suggesting its potential for broad application. Conclusions: This study demonstrates the effectiveness of a collaborative development process in creating a digital tool that addresses the mental health needs of children in crisis situations. The positive feedback from pediatricians indicated that the platform has the potential to become a valuable resource for early recognition, crisis intervention, and parental support in community pediatric settings. Future research will focus on broader implementation and assessing the platform's impact on clinical outcomes.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".