Capturing health literacy assessment in the electronic health record through evidence-based concept creation: A review of the literature and recommendations for action

Structural Determinants of Health Literacy Among Formerly Incarcerated Individuals: Insights From the Survey of Racism and Public Health

BACKGROUND

Formerly incarcerated individuals (FIIs) encounter difficulties with covering the cost of dental and medical care, adhering to medication regimens, and receiving fair treatment from health care providers. Yet, no published research has examined modifiable pathways to increase FIIs' health literacy (HL), which is essential for addressing the health needs of this vulnerable population.

OBJECTIVE

The aim of this article is to examine neighborhood characteristics (neighborhood deprivation, racial and economic polarization, and residential segregation) and public assistance program enrollment as structural determinants of limited health literacy (LHL) among FIIs.

METHODS

Using a socioecological framework, we analyzed a subsample of 578 FIIs from the 2023 Survey of Racism and Public Health, an online cross-sectional survey spanning U.S. Department of Health & Human Services Regions 1, 2, and 3. HL was assessed using the Brief Health Literacy Screen. Logistic regression models estimated unadjusted and adjusted associations of LHL with neighborhood characteristics and public assistance program enrollment. Adjusted models controlled for age, race and ethnicity, gender identity, educational attainment, marital and employment status, number of children, chronic health conditions, and incarceration length.

KEY RESULTS

The 578 FIIs had an average age of 46, with 42% having LHL. We observed a statistically significant association between public assistance program enrollment and LHL (unadjusted odds ratio [OR] = 2.72, 95% confidence interval [CI]: 1.87, 4.01; adjusted OR = 2.50, 95% CI: 1.62, 3.88). We found no statistically significant associations of LHL with neighborhood deprivation, racial and economic polarization, and residential segregation.

CONCLUSIONS

Our findings suggest that there may be an opportunity to develop tailored interventions for increasing HL among FIIs through public assistance programs. Dissemination of HL resources among this marginalized group can improve their self-management of chronic diseases. This is of paramount importance because FIIs must simultaneously navigate other challenges after incarceration (e.g., unstable housing). [HLRP: Health Literacy Research and Practice. 2025;9(1):e8-e18.].

The impact of electronic health literacy on emotional management ability among college students: the mediating roles of peer relationships and exercise self-efficacy

OBJECTIVE

This study aims to explore electronic health literacy's impact on college students' emotional management ability and analyze the chained mediating effects of peer relationships and exercise self-efficacy.

METHODS

The study used stratified, cluster, and staged sampling methods to collect demographic information. Electronic health literacy, peer relationships, exercise self-efficacy, and emotional management ability data were collected among college students using the Questionnaire Star software. A total of 30,105 valid questionnaires were obtained, with a valid response rate of 98.7%. Using Process Model 6, investigated how electronic health literacy sequentially influences emotional management ability through mediators such as peer relationships and exercise self-efficacy.

RESULTS

There was a significant positive correlation between electronic health literacy and emotional management ability (r = 0.496, P < 0.01), between electronic health literacy and peer relationships (r = 0.226, P < 0.01), and between electronic health literacy and exercise self-efficacy (r = 0.180, P < 0.01). Peer relationships were significantly positively correlated with exercise self-efficacy (r = 0.142, P < 0.01) and emotional management ability (r = 0.396, P < 0.01). Exercise self-efficacy was significantly positively correlated with emotional management ability (r = 0.339, P < 0.01). Electronic health literacy significantly and positively predicted emotional management ability (β = 0.254, P < 0.001). There was a mediating effect of peer relationships between electronic health literacy and emotional management ability, accounting for 58.2% of the impact, 95%CI: [0.036,0.042]. Exercise self-efficacy also mediated the relationship between electronic health literacy and emotional management ability, accounting for 61.5% of the effect, 95%CI: [0.022,0.027]. Moreover, a chained mediating effect of peer relationships and exercise self-efficacy was observed between electronic health literacy and emotional management ability, accounting for 17.1% of the effect, 95%CI: [0.004,0.005].

CONCLUSION

Electronic health literacy among college students can positively predict emotional management ability. Both peer relationships and exercise self-efficacy play a chained mediating role between electronic health literacy and emotional management ability.

The effect of health literacy and health empowerment on quality of life in the elderly

BACKGROUND

This study was conducted to determine the effect of health literacy and health promotion on quality of life in the elderly.

METHOD

This descriptive and cross-sectional study was conducted with 981 elderly individuals aged 65 years and older in Turkey. 'Introductory Information Form', 'Health Literacy Scale', 'Elderly Health Empowerment Scale' and 'Quality of Life Scale for the Elderly' were used to collect data. The research data were analyzed by Structural Equation Modelling using SPSS 22.0, AMOS V 24.0, G*Power 3.1 statistical package programs.

RESULTS

Health literacy level increased health empowerment level (t = 5.929 R²  = 0.035, P < 0.05). Health literacy level increased quality of life level (t = 13.439 R²  = 0.156, P < 0.05). Health empowerment level was found to affect quality of life level (t = 17.746 R²  = 0.243, P < 0.05). Health empowerment was found to have a mediating role in the effect of health literacy on quality of life (β = 0.502, 95% confidence interval (lower bound-upper bound) = 0.013-1.038). It was determined that the model created in line with the hypotheses was compatible and the model fit indices x² /SD = 4.919, root mean square error of approximation = 0.06, comparative fit index = 0.91, fit index = 0.95, adjusted fit index = 0.93, incremental fit index = 0.91 were within the desired limits.

CONCLUSIONS

As the level of health literacy increases, so does the level of health empowerment and quality of life. As the level of health empowerment increases, so does the level of quality of life. It is recommended to design policies that will help to increase the health literacy and health empowerment levels of the elderly. Longitudinal studies on quality of life in the elderly are recommended.

An evidence-based strategy to achieve equivalency and interoperability for social-behavioral determinants of health assessment, storage, exchange, and use

The interoperable exchange of social-behavioral determinants of health data is challenging due to complex factors including multiple recommendations, multiple tools with varying domains, scoring, and cutpoints, and lack of terminology code sets for storing assessments and findings. This article describes a strategy that permits scoring by social-behavioral determinants of health domain to create interoperability and equivalency across tools, settings, and populations. The three-tier scoring strategy converts social-behavioral determinants of health data to (1) be used immediately at point of care by identifying social needs or social risk factors, (2) be consumed within analytics and algorithms and for secondary analysis, and (3) produce total scores that reflect social determinant burden and behavioral determinant burden across populations and settings within a healthcare system. The strategy supports the six uses recommended by the National Academy of Medicine, provides flexibility in choice of social-behavioral determinants of health tool, and leverages the power of social-behavioral determinants of health data in healthcare delivery.