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Purpose: To examine racial and gender differences in treatment acceptance and relapse potential among rural residents admitted to a substance use disorder (SUD) treatment program. Methods: A cross‐sectional study using data collected from a sample of 1850 rural residents admitted to a South Carolina state‐run SUD treatment center between the years of 2018 and 2020. Chi‐square and t‐tests were used to compare treatment acceptance and relapse potential by race and gender. Multivariate logistic regression analyses was conducted to further examine the relationship of race and gender with treatment acceptance and relapse potential after adjusting for potential confounding variables. Findings: Approximately 50% of participants were classified as being accepting of their treatment and committed to changing their substance use, and there were no racial or gender differences in the bivariate and multivariate analyses. Approximately 25% of participants were classified as having low/no potential risk for relapsing, and there were no racial or gender differences in the bivariate analysis. However, the adjusted odds ratio of relapsing risk were lower among White compared to Black adults [AOR = 0.49 with 95% CI (0.31–0.77)]. Conclusion: This study suggests there are no gender or racial differences in treatment acceptance for SUD but that Black adults are at greater risk of relapsing relative to White adults. Additional research is needed to identify factors that increase Black adults' risk for relapse to inform interventions that can improve SUD treatment outcomes in this population. [ABSTRACT FROM AUTHOR]

Summary: Police officers experience exposures associated with increased inflammation, such as the stress associated with shiftwork and poor‐quality diet, both of which have been shown to affect sleep duration and quality. This study examined the longitudinal and cross‐sectional effects of the Energy‐density Dietary Inflammatory Index (E‐DII™) on objectively and subjectively measured sleep among police officers. Data were derived from the Buffalo Cardio‐Metabolic Occupational Police Stress Cohort (n = 464 at baseline), with longitudinal data collected from 2004 to 2019. A food frequency questionnaire obtained estimated dietary intake from which E‐DII scores were calculated. Dependent variables were objectively (Micro Motion Logger Sleep Watch™) and subjectively (Pittsburgh Sleep Quality Index) measured sleep quality and quantity. The analyses included a series of linear mixed‐effects models used to examine cross‐sectional and longitudinal associations between the E‐DII and sleep quantity and quality. Cross‐sectionally, more pro‐inflammatory diets were associated with higher wake‐after‐sleep‐onset but improved subjective sleep quality. In models accounting for both longitudinal and cross‐sectional effects, for every 1‐unit increase in the E‐DII scores over time (representing a pro‐inflammatory change), wake‐after‐sleep‐onset increased by nearly 1.4 min (p = 0.07). This result was driven by officers who primarily worked day shifts (β = 3.33, p = 0.01). Conversely, for every 1‐unit increase in E‐DII score, the Pittsburgh Sleep Quality Index global score improved. More pro‐inflammatory diets were associated with increased wake‐after‐sleep‐onset, an objective measure of sleep quality. Intervention studies to reduce dietary inflammatory potential may provide greater magnitude of effect for changes in sleep quality. [ABSTRACT FROM AUTHOR]

Aim: To clarify the meaning of the concept sleep quality. Background: Sleep loss and sleep quality are global health concerns. Poor sleep quality has significant adverse health outcomes. A clarification of the term is necessary to inform patients and healthcare providers, promote consistent theoretical and operational definitions in research, and develop prevention and treatment strategies. Design: Concept analysis. Data Sources: Scientific literature from electronic databases (CINAHL, PsycINFO, PubMED, Web of Science, and JSTOR) and definitions from online dictionaries. Review Methods: Rodgers' Evolutionary method was applied to guide the concept analysis to identify and determine the attributes, antecedents, and consequences. Results: Sleep quality is defined as an individual's self‐satisfaction with all aspects of the sleep experience. Sleep quality has four attributes: sleep efficiency, sleep latency, sleep duration, and wake after sleep onset. Antecedents include physiological (e.g., age, circadian rhythm, body mass index, NREM, REM), psychological (e.g., stress, anxiety, depression), and environmental factors (e.g., room temperature, television/device use), and family/social commitments. Good sleep quality has positive effects such as feeling rested, normal reflexes, and positive relationships. Poor sleep quality consequences include fatigue, irritability, daytime dysfunction, slowed responses, and increased caffeine/alcohol intake. Conclusions: Sleep quality is essential, and poor sleep quality contributes to disease and poor health outcomes. Given the extensive consequences of poor sleep quality, nurses and clinicians are vital in instructing the importance of good sleep. [ABSTRACT FROM AUTHOR]