The proposed project is designed to investigate the relative contributions of gender, race and ethnicity, and age to health care providers decisions about pain treatment. Considerable anecdotal and correlational data suggest that men and women receive different treatment for a variety of pain conditions. Similarly, there are disparities in the treatment of different racial groups with respect to pain treatment. Our pilot research has shown that older pain sufferers may be less likely to receive pain medication compared to younger patients. The application represents a competing renewal of our """"""""Sex vs. Gender in Pain Perception"""""""" grant which demonstrated that social learning, particularly genderrelated stereotypes and expectations about pain were powerful determinants of sex differences in pain responding. We also demonstrated that these gender-related expectations influenced the perception of others' pain. We propose to employ an innovative LENS model approach, delivered via the internet, to capture the decision policies of dentists, nurses, and physicians who will make decisions about multimedia case vignettes of """"""""Virtual Humans"""""""" with different presentations of pain. The LENS model methodology will allow for the determination of an individual's or group's statistically reliable predictors of the decisions about pain treatment. The Web-based delivery system will allow for the recruitment of larger sample sizes with sufficient representation of sexes, and racial groups for statistical analyses of differences between them. By employing a facial expression coding system we will be able to standardize the degree of pain expression across gender and race. Systematic variability in decisions about pain treatment will therefore be a function of the health care provider's biases and expectations. This methodology will allow for an examination of the contribution of these key variables and tests of specific hypotheses about social learning influences on provider's treatment decisions. The research addresses the public health need to understand, recognize and change gender, race, and age biases in the assessment and treatment of pain. The results will have direct implications for treatment and the training of health care professionals, particularly in the assessment of gender, race, and age biases in their decisions about pain treatment.
| Hirsh, Adam T; Callander, Sarah B; Robinson, Michael E (2011) Patient demographic characteristics and facial expressions influence nurses' assessment of mood in the context of pain: a virtual human and lens model investigation. Int J Nurs Stud 48:1330-8 |
| Hirsh, Adam T; Jensen, Mark P; Robinson, Michael E (2010) Evaluation of nurses' self-insight into their pain assessment and treatment decisions. J Pain 11:454-61 |
| Alqudah, Ashraf F; Hirsh, Adam T; Stutts, Lauren A et al. (2010) SEX AND RACE DIFFERENCES IN RATING OTHERS' PAIN, PAIN-RELATED NEGATIVE MOOD, PAIN COPING, AND RECOMMENDING MEDICAL HELP. J Cyber Ther Rehabil 3:63-70 |
| Hirsh, Adam T; Alqudah, Ashraf F; Stutts, Lauren A et al. (2008) Virtual human technology: capturing sex, race, and age influences in individual pain decision policies. Pain 140:231-8 |
