Demographic and childhood environmental predictors of resilience in a community sample
Introduction
Resilience refers to the ability to thrive in the face of stress and other adversity (Bonanno, 2004, Connor and Davidson, 2003). An individual’s level of resilience is likely determined by a wide variety of factors, with existing research suggesting that genetic (Caspi et al., 2003, Jabbi et al., 2007, Jang et al., 2007, Stein et al., in press, Tannenbaum and Anisman, 2003), biological (Charney, 2004, Morgan et al., 2002, Ozbay et al., 2008), psychological (Campbell-Sills et al., 2006, Tugade and Fredrickson, 2004), and environmental (Haskett et al., 2006, King et al., 1998) factors all play significant roles. Investigation of resilience is an important priority for psychiatric research because understanding positive adaptation to stress may aid in prevention and intervention efforts focused on helping individuals recover from stressful events and stress-related disorders (e.g. posttraumatic stress disorder).
Recently, investigation of resilience has been facilitated by the development and validation of self-report scales that measure individuals’ perceived abilities to bounce back from adversity. One instrument that has been validated in several studies is the Connor–Davidson Resilience Scale (CD-RISC) (Connor and Davidson, 2003, Campbell-Sills and Stein, 2007, Vaishnavi et al., 2007). Most investigations that have employed the CD-RISC have been conducted using specially selected samples such as patients with psychiatric disorders (Davidson et al., 2005), undergraduates (Campbell-Sills et al., 2006, Brown, 2008), and older adults (Lamond et al., 2008). Therefore, most available information regarding resilience as measured by the CD-RISC (e.g. its distribution and correlates) pertains to these specific subgroups.
The initial validation study of the CD-RISC administered the measure to a sizable community sample (Connor and Davidson, 2003). However, few conclusions could be drawn regarding the distribution and correlates of resilience in the general community because this sample was collapsed with several patient samples for most data analyses. The only other study to administer the CD-RISC to a community sample utilized the Chinese version of the scale (Yu and Zhang, 2007); however, the generalizability of the results to U.S. samples is questionable (e.g. the Chinese CD-RISC was found to have different psychometric properties than the English CD-RISC; Yu and Zhang, 2007).
To our knowledge, no other resilience scale has been administered to a large US community sample. One recent study reported on demographic predictors of resilience in the general community, albeit using methods other than a self-report scale to assess resilience (Bonanno et al., 2007). This study assessed predictors of resilience in a large community sample that lived in or around New York City during the September 11, 2001 terrorist attack. Resilience was operationally defined as experiencing fewer than two symptoms of posttraumatic stress disorder during the first six months after the terrorist attack. Using this definition of resilience, the investigators found that several demographic characteristics uniquely predicted resilient outcomes (e.g. male gender, Asian ethnicity and older age; Bonanno et al., 2007).
The primary aim of this study was to contribute further to the understanding of resilience in the general community by examining the relationship of self-reported resilience to demographic characteristics. Knowledge about demographic correlates of self-reported resilience could inform future studies that incorporate this mode of resilience assessment (e.g. by identifying potentially important covariates). In addition, it was of interest to determine whether the demographic effects reported by Bonanno et al. (2007) would be observed using a different methodology for assessing resilience. A secondary aim of this study was to explore the relationship of resilience to a hypothesized environmental risk factor for lower resilience: history of childhood maltreatment. To accomplish these objectives, we administered a short form of the CD-RISC (Campbell-Sills and Stein, 2007) and a questionnaire regarding experiences with childhood maltreatment (Bernstein et al., 2003) to a large sample of community survey respondents who also provided detailed information about demographic characteristics.
Section snippets
Participants
The resilience and childhood maltreatment scales were administered as part of a large population survey. Participants were respondents to the 2006 Victimization Survey on crime and justice issues conducted by the Mid-South Social Survey (MSSS) at the University of Memphis (Forde, 2006a). Respondents to the MSSS were 836 residents of metropolitan Memphis, Tennessee who had working telephone numbers and were 18 years of age or older. Comparisons of the final survey sample with US Census data for
Properties of the CD-RISC-10 in the MSSS sample
The mean score on the CD-RISC-10 was 31.78 (SD = 5.41; range = 9–40). The distribution of CD-RISC-10 scores is shown in Fig. 1. Scores were negatively skewed, with more individuals rating themselves on the upper extreme than the lower extreme of resilience. Despite this overall pattern, skewness (−0.61) and kurtosis (0.23) statistics fell within the range typical of normally distributed traits. The internal consistency of the scale was satisfactory (Cronbach’s α = .75).
Zero-order relationships of resilience to demographic variables
Table 1 shows a summary of
Discussion
This study contributes to the literature on resilience by examining demographic and childhood environmental predictors of resilience in the general community. Resilience was measured using a well-validated self-report instrument that assesses individuals’ perceptions of their abilities to recover effectively from adversity. Results suggested that approximately 11% of the variance in resilience was explained by demographic characteristics and an additional 2% of the variance in resilience was
Contributors
Dr. Campbell-Sills performed data analyses and wrote the initial draft of the manuscript. Dr. Forde contributed to study design, data collection, data analysis, and revision of the manuscript. Dr. Stein contributed to study design and revision of the manuscript.
Role of funding source
This study was funded in part by the Memphis Shelby Crime Commission, Memphis, Tennessee (Dr. Forde). The Memphis Shelby Crime Commission had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.
Conflicts of interest statement
None declared.
Acknowledgments
This study was funded in part by the Memphis Shelby Crime Commission, Memphis, Tennessee (Dr. Forde). The authors wish to thank Kathryn Connor and Jonathan Davidson for permitting us to use the Connor–Davidson Resilience Scale in this study.
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