Fostering cultural competence: Exploring client perception in a health coaching intervention among college students.

Health coaching could be an innovative approach to develop student coaches' cultural competence (CC) among future health professionals. The current mix-method study design explored the impact of an 8-week peer health coaching intervention among college students on CC, from both student health coaches (i.e., students majored in health sciences who completed health coaching training and acted as health coach) and student clients' perspective. Nine student coaches and 24 student clients participated in the study. The quantitative analysis showed an increase in the clients' perceived level of coaches' CC between the pre- and posttest. The qualitative analysis revealed three themes, including varying levels of awareness, respectful and culturally responsive coaching, and cultural connection. Implications and recommendations for educators and researchers are discussed.

An 8-Week Peer Health Coaching Intervention among College Students: A Pilot Randomized Study.

This study explored the effects of an 8-week peer coaching program on physical activity (PA), diet, sleep, social isolation, and mental health among college students in the United States. A total of 52 college students were recruited and randomized to the coaching (n = 28) or the control group (n = 24). The coaching group met with a trained peer health coach once a week for 8 weeks focusing on self-selected wellness domains. Coaching techniques included reflective listening, motivational interviews, and goal setting. The control group received a wellness handbook. PA, self-efficacy for eating healthy foods, quality of sleep, social isolation, positive affect and well-being, anxiety, and cognitive function were measured. No interaction effects between time and group were significant for the overall intervention group (all p > 0.05), while the main effects of group difference on moderate PA and total PA were significant (p < 0.05). Goal-specific analysis showed that, compared to the control group, those who had a PA goal significantly increased vigorous PA Metabolic Equivalent of Task (METs) (p < 0.05). The vigorous METs for the PA goal group increased from 1013.33 (SD = 1055.12) to 1578.67 (SD = 1354.09); the control group decreased from 1012.94 (SD = 1322.943) to 682.11 (SD = 754.89); having a stress goal significantly predicted a higher post-coaching positive affect and well-being, controlling the pre-score and other demographic factors: B = 0.37 and p < 0.05. Peer coaching showed a promising effect on improving PA and positive affect and well-being among college students.

Racism and Stress-Related Growth Among Asian Internationals: Ethnic Identity, Resilience, and Coping During COVID-19.

Experiences of anti-Asian discrimination following COVID-19 has deleterious effects on the mental health of Asian internationals residing in the United States. In this study, hierarchical regression models and Hayes' PROCESS models were used to examine the main effect and moderating effect of ethnic identity, coping strategy, and resilience on stress-related growth among Asian international students and workers (N = 237) in the United States who experienced racism during the pandemic. The findings indicated coping strategies and resilience were significantly associated with stress-related growth. Ethnic identity and coping strategies additionally moderated the link between the experience of racism and stress-related growth.

Genomic Common Data Model for Seamless Interoperation of Biomedical Data in Clinical Practice: Retrospective Study.

BACKGROUND: Clinical sequencing data should be shared in order to achieve the sufficient scale and diversity required to provide strong evidence for improving patient care. A distributed research network allows researchers to share this evidence rather than the patient-level data across centers, thereby avoiding privacy issues. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) used in distributed research networks has low coverage of sequencing data and does not reflect the latest trends of precision medicine. OBJECTIVE: The aim of this study was to develop and evaluate the feasibility of a genomic CDM (G-CDM), as an extension of the OMOP-CDM, for application of genomic data in clinical practice. METHODS: Existing genomic data models and sequencing reports were reviewed to extend the OMOP-CDM to cover genomic data. The Human Genome Organisation Gene Nomenclature Committee and Human Genome Variation Society nomenclature were adopted to standardize the terminology in the model. Sequencing data of 114 and 1060 patients with lung cancer were obtained from the Ajou University School of Medicine database of Ajou University Hospital and The Cancer Genome Atlas, respectively, which were transformed to a format appropriate for the G-CDM. The data were compared with respect to gene name, variant type, and actionable mutations. RESULTS: The G-CDM was extended into four tables linked to tables of the OMOP-CDM. Upon comparison with The Cancer Genome Atlas data, a clinically actionable mutation, p.Leu858Arg, in the EGFR gene was 6.64 times more frequent in the Ajou University School of Medicine database, while the p.Gly12Xaa mutation in the KRAS gene was 2.02 times more frequent in The Cancer Genome Atlas dataset. The data-exploring tool GeneProfiler was further developed to conduct descriptive analyses automatically using the G-CDM, which provides the proportions of genes, variant types, and actionable mutations. GeneProfiler also allows for querying the specific gene name and Human Genome Variation Society nomenclature to calculate the proportion of patients with a given mutation. CONCLUSIONS: We developed the G-CDM for effective integration of genomic data with standardized clinical data, allowing for data sharing across institutes. The feasibility of the G-CDM was validated by assessing the differences in data characteristics between two different genomic databases through the proposed data-exploring tool GeneProfiler. The G-CDM may facilitate analyses of interoperating clinical and genomic datasets across multiple institutions, minimizing privacy issues and enabling researchers to better understand the characteristics of patients and promote personalized medicine in clinical practice.

Network-based analysis of diagnosis progression patterns using claims data.

In recent years, several network models have been introduced to elucidate the relationships between diseases. However, important risk factors that contribute to many human diseases, such as age, gender and prior diagnoses, have not been considered in most networks. Here, we construct a diagnosis progression network of human diseases using large-scale claims data and analyze the associations between diagnoses. Our network is a scale-free network, which means that a small number of diagnoses share a large number of links, while most diagnoses show limited associations. Moreover, we provide strong evidence that gender, age and disease class are major factors in determining the structure of the disease network. Practically, our network represents a methodology not only for identifying new connectivity that is not found in genome-based disease networks but also for estimating directionality, strength, and progression time to transition between diseases considering gender, age and incidence. Thus, our network provides a guide for investigators for future research and contributes to achieving precision medicine.