Sentinel Project: a Digital Registry and Education Network for Child Maltreatment Protection – Study Protocol (Preprint)
Notice bibliographique
Résumé
<sec> <title>BACKGROUND</title> Child maltreatment is a major public health concern with long-term neurobiological and psychosocial consequences. The detection and reporting of suspected cases often remain fragmented, with significant variability across services and the absence of a unified surveillance system. Pediatricians also lack adequate digital tools and specialized training to support timely recognition and documentation. Although international evidence shows that integrated digital registries and structured educational programs enhance early identification and interprofessional coordination, no comparable model has yet been systematically implemented in Italy. The Sentinel project was developed to address these gaps through the combined introduction of a REDCap-based digital registry and a structured training program for pediatric healthcare professionals. </sec> <sec> <title>OBJECTIVE</title> This study aims to evaluate the usability, feasibility, and preliminary impact of an integrated surveillance and training system designed to improve the early detection, documentation, and reporting of suspected child maltreatment by pediatricians and healthcare professionals. </sec> <sec> <title>METHODS</title> This observational, exploratory, monocentric study will span 24 months and involve hospital and community pediatricians who voluntarily enroll and provide informed consent. The project includes two interconnected components: (1) the development and implementation of a secure, anonymized digital registry for standardized data collection on suspected maltreatment, and (2) a theoretical–practical training program delivered through lectures, e-learning modules, webinars, and hands-on sessions. Usability will be assessed using the System Usability Scale (SUS). Training effectiveness will be evaluated through pre–post knowledge tests, competency assessments, and qualitative feedback. Statistical analyses will include descriptive statistics, paired-sample tests, Poisson or negative binomial regression for changes in reporting rates, and multivariable models to identify predictors of training outcomes and registry usability. </sec> <sec> <title>RESULTS</title> We expect high usability of the digital registry, with mean SUS scores exceeding 80. Reporting rates of suspected maltreatment are anticipated to increase markedly following implementation. Training is expected to result in substantial improvements in knowledge, competencies, and satisfaction, enhancing professionals’ capacity to recognize and manage suspected maltreatment. The integrated system is expected to improve reporting completeness, timeliness, and interprofessional coordination. </sec> <sec> <title>CONCLUSIONS</title> The Sentinel project is expected to validate an innovative, scalable model that integrates digital surveillance with structured training to enhance early detection and management of child maltreatment. By standardizing data collection, strengthening professional competencies, and fostering collaboration across hospital and community settings, the project aims to support the development of a regional or national observatory and promote an evidence-based, system-wide cultural shift in child protection. </sec> <sec> <title>CLINICALTRIAL</title> ClinicalTrials.gov Identifier: NCT07250074 </sec>
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».