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Updated: Oct 29, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Psychometric Evaluation of the Screener for Intensifying Community Referrals for Health
Jia-Wen Guo1, Andrea S Wallace1, Brenda L Luther1
1College of Nursing, University of Utah, Salt Lake City, UT, USA.
Abstract:
This study examined the psychometric properties of the Screener for Intensifying Community Referrals for Health (SINCERE), a 10-item, low literacy screening tool developed to elicit social needs (e.g., transportation, housing) impacting patients' ability to engage in health-related activities. Patients seeking care in a tertiary care emergency department (ED) were invited to complete the SINCERE as part of registration processes, and were asked about their desire for follow-up by a partnering service provider offering low- and no-cost community resource referrals. A total of 5,081 patients completed screenings were included in this sample. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) identified and verified one-factor structure, suggesting that the SINCERE's 10-items are homogenous and measure one construct. The reliability of Cronbach's α and McDonald's ω were 0.89. Item Response Theory (IRT) suggested the SINCERE can effectively identify patients wishing referrals, or who have social needs. Moreover, patients who had two or more social needs were those willing to receive referrals after discharge. The SINCERE is a valid and reliable tool for measuring social needs for health, and should be considered as a screening option for practice interventions seeking to address social needs.
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