Akademska digitalna zbirka SLovenije - logo
E-viri
Celotno besedilo
Recenzirano Odprti dostop
  • Validation of the Pain Resi...
    Ankawi, Brett; Slepian, P Maxwell; Himawan, Lina K; France, Christopher R

    The journal of pain, 08/2017, Letnik: 18, Številka: 8
    Journal Article

    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. A modified version of the Pain Resilience Scale retained the original factor structure when tested in a chronic pain sample. Construct validity was supported by expected relationships with pain-related protective and vulnerability measures. Further, a model including positive pain constructs showed that pain resilience accounts for unique variability when predicting quality of life and pain intensity.