Shima Ali Sadia

Monroe University, New York, USA

Abstract Title:

Verified Action Packets: An Implementation Model for Translating Public Health Guidance into Community-Level Action

Biography:

Shima Ali Sadia is a registered physician in Bangladesh and U.S.-based public health professional with over ten years of clinical and preventive health experience. She identifies as a Healthycian, bridging medicine and public health. Her work focuses on preventive health education, chronic disease support, care coordination, and culturally responsive health communication. She applies her clinical expertise to advance evidence-based and equitable healthcare focusing data-driven public health for everyone.

Research Interests:

Emerging public-health threats and ongoing prevention needs can be identified through surveillance systems, program data, and community health assessments; however, timely information is not consistently translated into community-level action. Persistent barriers include limited health literacy, language discordance, low institutional trust, practical constraints, and the continued circulation of outdated or inaccurate information. A feasible implementation model is proposed to strengthen the last mile of public-health communication and to create conditions under which verified guidance may be more readily converted into action across both routine public-health practice and emergency response. The model is centered on the Verified Action Packet, a concise and auditable communication unit in which an authoritative source, target audience, jurisdiction, validity period, prioritized action, and local service or resource are specified. Each packet proceeds through eight operational stages: Detect, Verify, Authorize, Translate, Route, Deliver, Correct, and Evaluate. To support feasibility, initial implementation would be restricted to a defined jurisdiction, a limited network of trusted delivery partners, and selected public-health scenarios, such as vaccination, screening, testing, masking, treatment access, heat-risk precautions, or other time-sensitive prevention activities. Large language models may support constrained drafting, plain-language adaptation, formatting, and language adaptation; however, health facts, translation approval, and publication authorization remain human responsibilities. The model is designed to minimize personal-data collection while preserving an audit trail for message origin, approval, distribution, correction, and retirement. A preregistered, equity-centered pilot evaluation is outlined to assess comprehension, action, trust, accessibility, language concordance, correction speed, and subgroup reach. This framework offers a protocol for converting verified public-health guidance into practical, local, and measurable action.