Women Veterans’ Barriers to Care-Seeking for Cardiovascular Disease Prevention

Background Women veterans have a high prevalence of traditional and nontraditional risks for cardiovascular disease (CVD) including obesity and posttraumatic stress disorder. Experts from the U.S. Department of Veterans Affairs have called for actions to improve the cardiovascular health of this population. One approach is to assess women veterans’ barriers to care-seeking for CVD prevention, to inform future intervention research. Objective The objective of this study was to describe women veterans’ barriers to care-seeking for CVD prevention, guided by the theory of care-seeking behavior and concept awareness. Methods Using a cross-sectional, descriptive design, a national sample of 245 women veterans participated in an online survey about barriers to care-seeking. Participants provided narrative responses to open-ended items, endorsements to closed-ended items, and rankings of their top five barriers. Researchers conducted poststratification weighting of numerical data to reflect the women veteran population. Results Narrative responses described unaffordable and inaccessible services, feeling harassed or not respected in healthcare settings, and lack of awareness of risks for CVD. Frequently endorsed barriers were unaffordable and inaccessible services. Frequently ranked barriers were feeling not respected in healthcare settings and clinicians not recommending CVD prevention. Discussion Findings support concepts in theory of care-seeking behavior and concept awareness. Understanding women veterans’ barriers to care-seeking for CVD prevention can inform clinicians and researchers as they address these barriers.

Realist Approach to Qualitative Data Analysis

Background A realist approach has gained popularity in evaluation research, particularly in understanding causal explanations of how a program works (or not), the circumstances, and the observed outcomes. In qualitative inquiry, the approach has contributed to better theoretically based explanations regarding causal interactions. Objective The aim of this study was to discuss how we conducted a realist-informed data analysis to explore the causal interactions within qualitative data. Methods We demonstrated a four-step realist approach of retroductive theorizing in qualitative data analysis using a concrete example from our empirical research rooted in the critical realism philosophical stance. These steps include (a) category identification, (b) elaboration of context-mechanism-outcome configuration, (c) demi-regularities identification, and (d) generative mechanism refinement. Results The four-step qualitative realist data analysis underpins the causal interactions of important factors and reveals the underlying mechanisms. The steps produce comprehensive causal explanations that can be used by related parties—especially when making complex decisions that may affect wide communities. Discussion The core process of realist data analysis is retroductive theorizing. The four-step qualitative realist data analysis facilitates this theorizing by allowing the researcher to identify (a) patterns, (b) fluctuation of patterns, (c) mechanisms from collected data, and (d) to confirm proposed mechanisms.

Methodology for Analyzing Qualitative Data in Multiple Languages

Background Translation strategies are commonly used for qualitative interview data to bridge language barriers. Inconsistent translation of interviews can lead to conceptual inequivalence, where meanings of participants' experiences are distorted, threatening scientific rigor. Objectives Our objective is to describe a systematic method developed to analyze multilingual, qualitative interview data while maintaining the original language of the transcripts. Methods A literature review of translation strategies, cross-language, and multilingual qualitative research was conducted. Combined with criteria for qualitative content analysis and trustworthiness, the methodology was developed and used for a qualitative descriptive study. Results The study had interview data in both English and Spanish. The research team consisted of both native Spanish and English speakers, who were grouped based on language. Verbatim transcription of data occurred in the original languages. All codes were kept in English, allowing the research team to view the data set as a whole. Two researchers within each group coded each transcript independently before reaching a consensus. The entire research team discussed all transcripts, and finally, major themes were determined. Participants' quotes remained in the original language for publication, with an English translation included when needed. Discussion Analyzing transcripts in the original language brought forth cultural themes that otherwise may have been overlooked. This methodology promotes conceptual equivalence and trustworthiness that is paramount in cultural, linguistic, and social determinants of health research to advance health equity.