Journal of Clinical and Translational Science最新文献

Introduction: Self-efficacy (or the belief in one's ability to effect change) often moderates the relationship between education, interest, and actions in evaluations of training programs that prepare community-based investigators in the clinical and translational sciences workforce. Such evaluations, however, tend to emphasize individual-level attitudes when there are also community- or organizational-level outcomes impacted.

Methods: This study uses a novel sequential, explanatory mixed-methods design to explore multiple levels of self-efficacy (or self-awareness of personal growth in leadership) in the Clinical Scholars program, an equity-centered leadership development program for mid- to later-career healthcare professionals. Our design involves: (1) bivariate correlations and confirmatory factor analysis of self-assessed competencies across all program participants to identify emergent combinations of competencies, which informed (2) more nuanced thematic coding of participants' stories of most significant change in their personal and professional lives, as a result of the program.

Results: In unpacking their accounts of personal leadership styles (that aligned with our quantitative analyses of competencies), we found that participants demonstrated multiple competencies simultaneously. Specifically, they employed emotionally intelligent learning and consensus-building dialogue to manage conflict for interpersonal impact. Additionally, they used this combination of skills to unite diverse stakeholders under a shared vision in order to lead and manage organizational change where all colleagues' contributions were valued.

Conclusion: Together, these methods extend our understanding of personal growth in leadership as an outcome of the program in terms of individual- and organizational-level impacts, using representative quantitative self-assessments to categorize rich qualitative descriptions.

Introduction: Social determinants of health (SDOH) are an important contributor to health status and health outcomes. In this analysis, we compare SDOH measured both at the individual and population levels in patients with high comorbidity who receive primary care at Federally Qualified Health Centers in New York and Chicago and enrolled in the Tipping Points trial.

Methods: We analyzed individual- and population-level measures of SDOH in 1,488 patients with high comorbidity (Charlson Comorbidity Index ≥ 4) enrolled in Tipping Points. At the individual level, we used a standardized patient-reported questionnaire. At the population level, we employed patient addresses to calculate the Social Deprivation Index (SDI) and Area Deprivation Index. Multivariable regressions were conducted in addition to qualitative feedback from stakeholders.

Results: Individual-level SDOH are distinct from population-level measures. Significant component predictors of population SDI are being unhoused, unable to pay for utilities, and difficulty accessing medical transportation. Qualitative findings mirrored these results. High comorbidity patients report significant SDOH challenges at the individual level. Fitting a binomial generalized linear model, the comorbidity score is significantly predicted by the composite individual SDOH index (p < 0.0001) controlling for age and race/ethnicity.

Conclusions: Individual- and population-level SDOH measures provide different risk assessments. The use of community-level SDI data is informative in the aggregate but should not be used to identify patients with individual unmet social needs. Health systems should implement a standardized individualized assessment of unmet SDOH needs and build strong, enduring partnerships with community-based organizations that can provide those services.

Introduction: There is increasing pressure on the federal research budget and shifting public opinions about the value of the academic enterprise. We must develop and apply metrics that demonstrate the broad benefits of research for health and society. The Translational Science Benefits Model (TSBM) measures the impact of large-scale translational science initiatives, such as the National Cancer Institute's Cancer Moonshot. TSBM provides the scaffolding to illustrate how science has real-world health impacts. We propose an expansion of the TSBM to explicitly include implementation-focused outcomes.

Methods: TSBM includes four categories of benefits, including (1) clinical and medical, (2) community and public health, (3) economic, and (4) policy and legislative. Implementation science outcomes serve as a precursor to the model's established domains of impact and can help to sharpen focus on the translational steps needed to achieve a broad range of impacts. We provide several examples of studies that illustrate these implementation outcomes and other clinical and community benefits.

Conclusions: It is important to consider a broad range of scientific impacts and the conditions that are necessary to achieve them. The expansion of the TSBM to include implementation science outcomes may help to accelerate the cancer community's ability to achieve the goal of preventing 4 million cancer deaths by 2047.

[This corrects the article DOI: 10.1017/cts.2022.381.].

Background: Researchers and research organizations acknowledge the importance of paying research participants but often overlook the process of providing participant payments as a locus for improving equity and inclusion in clinical research. In this conceptual paper, we argue that participants' lived experiences and social context should be recognized and respected when developing these processes.

Methods: We consider how participant payment processes that require specific payment types, delay the timing of payment, or require sharing sensitive information may impose barriers to equitable research. Building on findings from empirical research of participants' perspectives on respect in research and a relational ethics framework of person-oriented research ethics, we explore how researchers and research organizations can better demonstrate respect through the research participation payment process.

Results: We propose five considerations for demonstrating respect when providing payment: (1) practice cultural humility, (2) be mindful of socioeconomic factors, (3) be flexible, (4) be transparent, and (5) maintain open communication. These considerations are intended to address the lack of existing ethical guidance around the process for participant payments and promote more inclusive clinical research. We provide a set of sample questions for research teams to consider how they could modify their payment processes to better demonstrate respect.

Conclusions: By better demonstrating respect for participants when providing payment, researchers can work toward ensuring that their research procedures are more inclusive, respond to the needs of diverse communities, and result in more equitable relationships with participants.

The Master of Science in Clinical Research Management program at Rutgers Biomedical Health Sciences underwent significant restructuring aligned with the Clinical and Translational Science Award funding parameters. This evolution necessitated formal evaluation through accreditation by the Commission on Accreditation of Allied Health Education Programs. The years-long accreditation process posed challenges, particularly regarding the collection of course learning outcomes data aligned with accreditation competency standards. The objective of this special communication is to report the rationale behind pursuing accreditation for clinical research degrees, the data collection challenges during the accreditation process, and a potential solution. In order to address existing university metric data gaps, Research Electronic Data Capture (REDCap) software was used to develop a data collection tool that streamlined the accreditation process and reduced the administrative burden. REDCap was effective in allowing faculty to self-report 3 years of course outcomes data for accreditation. There was an elevated level of user satisfaction compared to alternative data collection methods. A SWOT analysis identified the strengths and weaknesses of using REDCap, emphasizing strengths in functionality that include customizability, data validation, and compliance with regulatory standards. Overall, the advantages of leveraging REDCap for accreditation data collection, including customization, data security, and user-friendliness outweigh the key disadvantage of REDCap, which is its limited reporting capabilities.