Sentinel Project: a Digital Registry and Education Network for Child Maltreatment Protection – Study Protocol (Preprint)
Bibliographic record
Abstract
BACKGROUND Child maltreatment, a major public health concern, has long-term neurobiological, psychological, and social consequences. Recognizing, documenting, and reporting suspected cases should be systematic across hospital and community services, but these processes are hindered by inconsistent surveillance systems, uneven professional training, and the lack of standardized data collection tools. Timely follow-up, updated clinical criteria, and structured multidisciplinary training can improve case assessment, traceability, and professionals’ ability to report suspected maltreatment. The Sentinel project was developed to address these clinical, organizational, and public health gaps through a combined REDCap-based digital registry and structured training program for pediatric health care professionals. OBJECTIVE 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 health care professionals. METHODS This is an observational, exploratory, monocentric pilot implementation study with a pretest-posttest evaluation design. The study will be conducted over 24 months in a hospital-community pediatric network. Voluntary participants will be hospital and community pediatricians and other pediatric health care professionals who provide written informed consent. The project includes two interconnected components: (1) implementation of a secure, anonymized REDCap registry for standardized documentation of suspected maltreatment, follow-up, reporting pathways, and case management outcomes and (2) a theoretical-practical training program delivered through lectures, e-learning modules, webinars, supervised exercises, and hands-on registry sessions. Primary and secondary outcomes will be assessed at prespecified time points, including baseline, immediately after training, during registry use, and at the end of follow-up. Registry usability will be measured using the System Usability Scale; training effectiveness will be measured through pretest-posttest knowledge, competency tests, and satisfaction questionnaires; and reporting outcomes will be measured through comparison of pre- and postimplementation reporting rates. RESULTS The study will report the usability, feasibility, and preliminary implementation outcomes of the integrated registry training system. Main results will include System Usability Scale scores, registry completion indicators, reporting timeliness, the number and rate of suspected maltreatment reports before and after implementation, changes in knowledge and competency scores, participant satisfaction, and qualitative feedback on barriers to and facilitators of implementation. Participant recruitment and data collection began in April 2026 and are currently ongoing. Completion of data collection and publication of the results are expected between December 2027 and January 2028. CONCLUSIONS The Sentinel project will test an innovative and scalable pilot model that integrates digital surveillance with structured professional training to enhance the early detection, documentation, and management of suspected child maltreatment. By standardizing data collection, strengthening professional competencies, and fostering collaboration across hospital and community settings, the study will inform the development of a regional or national observatory and support an evidence-based, system-wide approach to child protection. CLINICALTRIAL ClinicalTrials.gov NCT07250074; https://clinicaltrials.gov/study/NCT07250074 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/89128
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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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.095 | 0.025 |
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".