Sentinel Project: a Digital Registry and Education Network for Child Maltreatment Protection – Study Protocol (Preprint)
Bibliographic record
Abstract
<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>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".