19.05.26 (inclusive)

by agreement of the parties

Idea

ICT tasks

Robotics

Information processing and transformation

Software/ IS

There is a deep systemic crisis of traditional methods of evaluating the work of government agencies, which is characterized by the transition from real protection of children's interests to formal reporting. Each department: the police, education, and healthcare operates with its own data and reporting, which does not allow us to see a complete picture of a child's life. The existing prevention system often works only when an offense has already been committed, that is, it works on the fact of the event, and not ahead of time. At the same time, conventional assessment systems did not take into account the difference in infrastructure between large cities and remote villages, which leads to unfair criticism of local executive authorities in those areas where the standard of living is objectively lower due to the lack of social facilities, rather than because of the poor work of employees. As a result, the lack of an objective, mathematically verified monitoring tool, such as the "Child Welfare Index", makes public administration in the field of childhood ineffective.

1. Pilot Scope of Work (MVP), so that potential performers can correctly assess their resources and propose solutions.; As a pilot scope of work (MVP), it is proposed to implement an updated performance assessment system that will test the mathematical model and automation of data collection without full-scale deployment for all 56 indicators. Below is a detailed list of works for the MVP stage.: 1. Methodological core: Setting up a mathematical model The central element of the MVP should be the programmatic implementation of the adjusted formula for the final Child Welfare Index (CWIfinal). Normalization algorithm: Implementation of linear scaling (min-max) procedures to bring heterogeneous data to the range 0-100. Implementing a formula with a correction factor: The adjusted formula will look like this: This formula is a calculation of the final condition index (CWI), which is adjusted based on regional coefficients. D1 + D2 + … Dn Kreg CWIfinal = ----------------- x --------------- x α n 100 where: CWIfinal is the final (adjusted for infrastructure) Child Welfare Index; D is the value of each direction (domain) of the base Index; n is the total number of evaluated directions.; Kreg - The level of facilities and services in a locality (as a percentage, from 0 to 100); α (alpha) is the coefficient of infrastructural elasticity (recommended from 0.3 to 0.5), which sets the mathematical weight of the influence of the material base on the final result. This integration fully preserves the original logic of calculating the basic part (arithmetic mean), but at the same time automatically reduces the final indicator if the actual availability of facilities (Kreg) does not meet the approved regional standards. Calibration of penalty functions: Setting up a model of the "normative-target method", in which low availability of infrastructure automatically reduces the final KPI of the region. 2. Technological integration: "A single source of truth" Within the framework of the MVP, it is necessary to abandon manual data entry and set up automatic sampling of information for key indicators through the QazTech platform. Integration with Smart Data Ukimet: Setting up data exchange for automatic scoring of family well-being based on the "Digital Family Card". Automation of 13 priority indicators: The transfer of subjective survey data into the category of "digital traces" in the following areas: Health: Data on screenings and injuries from the Damu Med system. Security: Crime statistics directly from the ERDR (General Prosecutor's Office). Education: Verification of the coverage of clubs and school meals through the NOBD and biometric systems. Infrastructure: GIS analysis of transport accessibility (15-minute radius to bus stops) and availability of playgrounds. 3. Regional pilot: A differentiated approach To test the hypothesis of "spatial heterogeneity" (uneven conditions), two contrasting regions should be included in the pilot.: The acceptor region (for example, Astana or Mangystau region): Testing the model in conditions of high migration load and pressure on the infrastructure. Donor region (for example, North Kazakhstan region): Assessment of effectiveness in a stable or declining population. The goal: To confirm that the environment difficulty coefficient (alpha) fairly adjusts the rating of akims, protecting them from "punitive statistics." 4. Visualization: Executive Dashboard Development of a real-time monitoring interface that includes: Dynamic indicators: Automatic illumination of risk zones (for example, a sharp increase in bullying or a shortage of student places). Predictive module: Using machine learning algorithms to identify correlations between infrastructure availability and juvenile delinquency. Comparative analysis: The possibility of comparing regions not by "raw" figures, but by the degree of managerial resistance of the environment. 2. Supplement the technical part with basic parameters: intended data sources, integration approaches, as well as key roles of system users; The technical architecture of the system is based on the principle of a "Single Source of Truth" (SSOT) and excludes manual generation of departmental certificates. The following are additions to the technical part of the project: I. Intended data sources For the automatic calculation of the Child Welfare Index and the KPI assessment of authorized bodies, the system must aggregate data from the following departmental information systems: Medical Sector (Ministry of Health of the Republic of Kazakhstan): Damu Med information system for obtaining data on preventive examinations, screenings, psychoemotional state and patterns of childhood injuries. Educational Sector (MP RK): A National Educational Database (NOBD) for verifying academic performance, enrollment in additional education, quality of school meals, and incident reporting (bullying). The Law Enforcement Sector (SOE RK): A unified register of Pre-trial Investigations (ERDR) for obtaining objective statistics on offenses against children and juvenile delinquency. Social Sector: A digital family map for automatic scoring of household well-being based on data from more than 30 sources. Infrastructure sector (GIS): Geoinformation systems and data from the Unified State Register of Real Estate (EGNR) for calculating transport accessibility and availability of living space. Financial Sector (OFD): Data from fiscal data operators for analyzing consumption patterns (for example, daily consumption of fruits and vegetables). Common analytical layer: Smart Data Ukimet (SDU), which functions as a "Data Lake" for consolidating information from 100+ databases. II. Approaches to integration The integration strategy is based on the national QazTech platform, which ensures technological sovereignty and algorithmic unification. Platform model (PaaS/IaaS): Using a single technology stack (Istio, Kafka, Service Mesh) to ensure network fault tolerance of at least 99.95%. Smart Bridge Gateway: Using a secure gateway to ensure seamless interoperability between departmental databases. Security and depersonalization: Mandatory hashing of personal data when transferring to the analytical module to protect the rights of citizens. Predictive analytics: Implementation of Causal Machine Learning methods to identify hidden correlations between support measures and real changes in the level of offenses. III. Key User roles The system provides a three-level role model to provide targeted access to analytics.: User level Role in the system Main functionality Senior Management (Strategic) The Presidential Administration, the Prime Minister, and Ministers Use the Executive Dashboard to monitor macro indicators and make strategic decisions. "Regional leadership" (Managerial) Akims of regions and cities, heads of departments, Control of regional KPIs, identification of "bottlenecks" in infrastructure and management of regional budgets "for children". Executive level (Operational) Members of mobile groups, guardianship officials, psychologists, inspectors Work with specific cases through a "Digital Family Card", receiving risk notifications (alerts) and maintaining rehabilitation plans. Methodological level (Analytical) Institute "Orken", researchers have access to depersonalized Big Data for the development of new anti-bullying modules and psychometric techniques. These parameters transform the system from a tool of retrospective reporting into a proactive mechanism for managing the well-being of the nation. Which of the listed data sources are the most critical for your current planning stage?

Ismailbaev Timurlan Shamileovich