A Deterministic Framework for Transforming Multi-Informant Observational Data into Structured Descriptors: A Feasibility Study

Background Multi-informant observational data obtained from parents and educators provide rich but context-dependent information. However, these data are rarely transformed into structured representations that can be consistently shared and interpreted across contexts without relying on diagnostic classification. Objective This study aimed to propose and implement a deterministic framework for transforming multi-informant observational data into a structured descriptor sets of structured, interpretable descriptors, and to examine its feasibility under controlled conditions. The framework targets an intermediate representation layer for structuring observational data prior to interpretation or decision-making. Methods Parent- and educator-reported questionnaire data were used as standardized inputs to a predefined deterministic framework. Child-centered observational signals were derived from parent reports, whereas educator-derived signals were incorporated as distinct contextual information sources. The framework applied transparent rule-based transformation procedures to generate predefined context-preserving descriptors without reliance on machine learning or statistical modeling. Structural behavior was examined using controlled simulated input conditions. Results The framework consistently generated a limited and prioritized set of descriptors for each input profile. Outputs were structurally stable and reproducible across all predefined input configurations, demonstrating consistent transformation of multi-informant data into structured representations. Conclusions This study demonstrates the feasibility of a deterministic framework for organizing multi-informant observational data into structured, non-diagnostic descriptors. By introducing a reproducible intermediate organizational layer, the framework provides a transparent approach to cross-context information structuring, with other multi-context observational settings involving multi-informant data integration.