Digital Platform for Pediatric Mental Health Support During Armed Conflicts: Development and Usability Study
Notice bibliographique
Résumé
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.
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 | 0,014 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».