Searching across hundreds of databases
Psychosocial factors that protect against negative outcomes for individuals with chronic pain have received increased attention in recent years. Pain resilience, or the ability to maintain behavioral engagement and regulate emotions as well as cognitions despite prolonged or intense pain, is one such factor. A measure of pain-specific resilience, the Pain Resilience Scale, was previously identified as a better predictor of acute pain tolerance than general resilience. The present study sought to validate this measure in a chronic pain sample, while also furthering understanding of the role of pain resilience compared with other protective factors. Participants with chronic pain completed online questionnaires to assess factors related to positive pain outcomes, pain vulnerability, pain intensity, and quality of life. A confirmatory factor analysis confirmed the 2-factor structure of the Pain Resilience Scale previously observed among respondents without chronic pain, although one item from each subscale was dropped in the final version. For this chronic pain sample, structural equation modeling showed that pain resilience contributes unique variance to a model including pain acceptance and pain self-efficacy in predicting quality of life and pain intensity. Further, pain resilience was a better fit in this model than general resilience, strengthening the argument for assessing pain resilience over general resilience.
Pubmed ID: 28428092 RIS Download
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Statistical modeling program that provides a wide choice of models, estimators, and algorithms in a program that has graphical displays of data and analysis results. Mplus allows the analysis of both cross-sectional and longitudinal data, single-level and multilevel data, data that come from different populations with either observed or unobserved heterogeneity, and data that contain missing values. Analyses can be carried out for observed variables that are continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types. In addition, Mplus has extensive capabilities for Monte Carlo simulation studies, where data can be generated and analyzed according to any of the models included in the program. The Mplus modeling framework draws on the unifying theme of latent variables. The generality of the Mplus modeling framework comes from the unique use of both continuous and categorical latent variables. Continuous latent variables are used to represent factors corresponding to unobserved constructs, random effects corresponding to individual differences in development, random effects corresponding to variation in coefficients across groups in hierarchical data, frailties corresponding to unobserved heterogeneity in survival time, liabilities corresponding to genetic susceptibility to disease, and latent response variable values corresponding to missing data. Categorical latent variables are used to represent latent classes corresponding to homogeneous groups of individuals, latent trajectory classes corresponding to types of development in unobserved populations, mixture components corresponding to finite mixtures of unobserved populations, and latent response variable categories corresponding to missing data.
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
scicrunch.org
