Food Intake, Source, and Planning and Shopping Behavior Differences Among Hispanic, White, Black, and Asian Females

BACKGROUND

Abdominal obesity remains a high public health concern. Within the United States, there are noted disparities among different ethnic/racial groups in relation to obesity, especially for females.

PURPOSE

The purpose of this secondary analysis project was to examine the differences in nutritional intake, food sources, and meal planning and food shopping between Hispanic, White, Black, and Asian females by abdominal obesity level in the United States.

METHODS

The 2017-2018 National Health Nutrition Examination data was used. Major variables included race/ethnicity, waist circumference (WC), nutritional intake, food source, and food shopping and meal planning behaviors. Descriptive statistics, correlational analyses, a series of two-way factorial analysis of variance, and odds ratio analyses were conducted to address research questions.

FINDINGS

When comparing nutritional intake and food source by different racial/ethnic groups and abdominal obesity level, there were no interaction effects for all categories across groups. However, for the racial/ethnic main effects and obesity main effects, significant differences among groups were noted for nutritional intake and food source categories. There were no differences in food shopping and meal preparation between abdominal obesity and non-obese participants in each racial/ethnic group.

CONCLUSIONS

Similarities and differences were noted between racial/ethnic groups for nutritional intake and sources of food. However, no significant differences were noted between racial/ethnic groups for food shopping and meal preparation behaviors. More research should be done to confirm these findings and further understand food shopping and meal preparation behaviors.

Race and gender disparities in preventive health activity engagement of older adults in the southeastern United States

A cross-sectional study was conducted to determine preventive-health-activity engagement in community-dwelling older adults participating in student-led health screenings in east Alabama. From 2017-2019, health professions students conducted health screenings at 23 community and independent living sites to assess medical and social needs of adults. Clients' responses to questions regarding vaccinations (flu/pneumonia/shingles), cancer screenings (colon/sex-specific), and other (dental/vision) screenings were aggregated to create a preventive health behavior (prevmed) score. Chi-square, t-tests, and regression analyses were conducted. Data from 464 adults ages 50-99 (72.9±10.1) years old were analyzed. The sample was 71.3% female, 63.1% Black/African American (BA), and 33.4% rural. Linear regression indicated BA race (p=0.001), currently unmarried (p=0.030), no primary care provider (p<0.001) or insurance (p=0.010), age <65 years (p=0.042) and assessment at a residential site (p=0.037) predicted lower prevmed scores. Social factors predict preventive health activity engagement in community-dwelling adults in east Alabama, indicating several opportunities to improve health outcomes.

Achievement of interprofessional competencies in live and virtual community clinics: A comparative study

BACKGROUND

Interprofessional education is imperative for training future healthcare professionals. While barriers exist within and across institutions to implement and sustain effective interprofessional education experiences for students, virtual clinics utilizing electronic health records may provide comparable benefits to in-person clinics.

OBJECTIVE

To determine whether differences in pre- and post-test self-assessments of interprofessional collaborative competencies are different between in-person and virtual clinics.

DESIGN

Pretest-posttest design utilizing the Interprofessional Collaborative Competencies Attainment Survey (ICCAS) before and immediately after participating in clinics, virtual or in-person.

SETTING

A large, public university in the southeastern United States.

PARTICIPANTS

Senior nursing students, third-year pharmacy students, senior nutrition/dietetics students, and undergraduate and graduate social work students.

METHODS

This study was conducted evaluating five cohorts of students engaged in interprofessional education clinics. Two cohorts completed in-person community clinics in 2019. In March 2020, the interprofessional education program adopted virtual clinics (three cohorts) utilizing pre-selected electronic health record cases. Student responses from the 20-item ICCAS, which was completed before and immediately after clinics, were aggregated into interprofessional competency subscale scores (communication, collaboration, roles and responsibilities, collaborative patient/family-centered approach, conflict management/resolution, and team functioning) and a total ICCAS score. Two-way ANOVA assessed Pre-Post and Mode (in-person vs. virtual) on total ICCAS score. t-tests compared Pre-Post ICCAS scores for each Mode.

RESULTS

Effects of Pre-Post (p < 0.001), but not Mode (p = 0.523), were observed on Total ICCAS scores. All ICCAS subscale scores were significantly higher in Post compared to Pre regardless of Mode.

CONCLUSIONS

Virtual interprofessional education clinics confer similar benefits to interprofessional collaborative competencies in healthcare professions students compared to in-person community clinics. Thus, modality offers flexibility for interprofessional education and provided several benefits over the in-person clinic approach.

Examining the Impact of Structured, Reflective Prebriefing on Student Performance During Simulation

Little is known about the impact of prebriefing on students' experiences of learning with simulation. This mixed-methods study evaluated the impact of prebriefing activities on nursing students' satisfaction, confidence, and performance of nursing skills during a simulation. Findings revealed students who experienced a structured, more robust prebriefing had improved performance during the simulation and reported higher levels of confidence and satisfaction in learning compared to a group that experienced a standard prebriefing. Findings are significant to the profession, they support the incorporation of structured, reflective prebriefing activities in simulation-based experiences.

A classification algorithm to predict chronic pain using both regression and machine learning - A stepwise approach

This secondary data analysis study aimed to (1) investigate the use of two sense-based parameters (movement and sleep hours) as predictors of chronic pain when controlling for patient demographics and depression, and (2) identify a classification model with accuracy in predicting chronic pain. Data collected by Oregon Health & Science University between March 2018 and December 2019 under the Collaborative Aging Research Using Technology Initiative were analyzed in two stages. Data were collected by sensor technologies and questionnaires from older adults living independently or with a partner in the community. In Stage 1, regression models were employed to determine unique sensor-based behavioral predictors of pain. These sensor-based parameters were used to create a classification model to predict the weekly recalled pain intensity and interference level using a deep neural network model, a machine learning approach, in Stage 2. Daily step count was a unique predictor for both pain intensity (75% Accuracy, F1 = 0.58) and pain interference (82% Accuracy, F1 = 0.59). The developed classification model performed well in this dataset with acceptable accuracy scores. This study demonstrated that machine learning technique can be used to identify the relationship between patients' pain and the risk factors.