Introduction: The absence of a unified conceptual framework for analyzing algorithmic violence against children and adolescents represents a critical gap in public health. Although Developmental Algorithmic Violence (DAV) is characterized by the exploitation of neurobiological and psychosocial vulnerabilities during critical periods of development by commercial algorithmic systems, a screening instrument for identifying vulnerability to this condition is non-existent. This study addresses this gap by describing the creation of the Multidimensional Instrument for Vulnerability to Developmental Algorithmic Violence (IMVAD). Methodology: A theoretical methodological development study based on a systematic literature review following the PRISMA protocol and critical analysis of secondary epidemiological data from national and international sources, with methodological triangulation of evidence. Results: The IMVAD prototype was structured into a four-dimensional architecture, totaling 27 operational indicators. Each indicator is scored on a three-level ordinal scale (0-1-2 points), generating a normalized index (0–100%) with classification into four vulnerability strata (Low 0–20%, Moderate 21–50%, High 51–75%, Very High 76–100%). Conclusions: Based on the theoretical, conceptual and methodological foundations, the initial IMVAD prototype was developed. Systematized for screening vulnerabilities to developmental algorithmic exploitation, the instrument was designed for digital application. Trained professionals administer the structured interview (8 to 12 min) using an online interface accessible via a link, where an algorithm processes the data in real time and stratifies the results into four risk levels. The IMVAD addresses a critical gap in epidemiological surveillance of digital neurodevelopmental disorders and finds the necessary legal framework for the development of digital child and adolescent health surveillance systems in the Digital ECA Law (Lei 15.211/2025). Although we have created an instrument proposal based on scientific concepts and data, its future validation and implementation will be essential for building digital health indicators and developing evidence-based preventive public policies.
Author Contributions
Conceptualization, A.C.C.R.; writing—original draft preparation, A.C.C.R.; writing—review and editing, A.C.C.R. and J.L.L.Z.; supervision, J.L.L.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
No new data were created or analyzed in this study. Data sharing is not applicable to this article.
Conflicts of Interest
The authors declare no conflict of interest.
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