生物心理社会模型
慢性疼痛
接受和承诺疗法
心理干预
注意
医学
认知行为疗法
生物反馈
苦恼
临床心理学
干预(咨询)
心理学
认知
精神科
心理治疗师
作者
Mary Driscoll,Robert R. Edwards,William C. Becker,Ted J. Kaptchuk,Robert D. Kerns
标识
DOI:10.1177/15291006211008157
摘要
The high prevalence and societal burden of chronic pain, its undertreatment, and disparities in its management have contributed to the acknowledgment of chronic pain as a serious public-health concern. The concurrent opioid epidemic, and increasing concern about overreliance on opioid therapy despite evidence of limited benefit and serious harms, has heightened attention to this problem. The biopsychosocial model has emerged as the primary conceptual framework for understanding the complex experience of chronic pain and for informing models of care. The prominence of psychological processes as risk and resilience factors in this model has prompted extensive study of psychological treatments designed to alter processes that underlie or significantly contribute to pain, distress, or disability among adults with chronic pain. Cognitive-behavioral therapy is acknowledged to have strong evidence of effectiveness; other psychological approaches, including acceptance and commitment therapy, mindfulness, biofeedback, hypnosis, and emotional-awareness and expression therapy, have also garnered varying degrees of evidence across multiple pain conditions. Mechanistic studies have identified multiple pathways by which these treatments may reduce the intensity and impact of pain. Despite the growing evidence for and appreciation of these approaches, several barriers limit their uptake at the level of organizations, providers, and patients. Innovative methods for delivering psychological interventions and other research, practice, and policy initiatives hold promise for overcoming these barriers. Additional scientific knowledge and practice gaps remain to be addressed to optimize the reach and effectiveness of these interventions, including tailoring to address individual differences, concurrently addressing co-occurring disorders, and incorporating other optimization strategies.
科研通智能强力驱动
Strongly Powered by AbleSci AI