Patient demographic characteristics and facial expressions influence nurses’ assessment of mood in the context of pain: A Virtual Human and lens model investigation

Abstract

Background Sex, race, and age disparities in pain assessment and treatment have been reported in the literature. However, less is known about how these demographic characteristics influence nurses’ assessment of the emotional experiences of patients who are in pain. Objectives To investigate the influence of patient demographic characteristics and facial expressions on nurses’ assessment of patient mood in the context of pain. Design A cross-sectional study employing Virtual Human (VH) technology and lens model methodology. Settings The current study was delivered via the internet. Participants Participants consisted of 54 registered nurses currently engaged in clinical practice. Nurses were recruited from healthcare settings across the United States. Methods Nurses viewed 32 patient vignettes consisting of a video clip of the VH patient and text-based clinical summary information describing a post-surgical context. Patient sex, race, age, and facial expression of pain were systematically manipulated across vignettes. Participants made positive and negative mood assessment ratings on computerized visual analogue scales. Idiographic multiple regression analyses were used to examine the patient characteristics that were significant predictors of nurses’ assessment ratings. Nomothetic paired samples t-tests were used to compare ratings within cue for the entire sample. Results The results of idiographic and nomothetic analyses indicated that VH sex, race, age, and facial expression cues were significant predictors of the mood assessment ratings of many nurses. The age cue had the largest impact among the demographic variables. Conclusions The results of the current study suggest that patient demographic characteristics and facial expressions may influence how nurses assess patient emotional status in the clinical pain context. These findings may lead to greater awareness by individual nurses and nursing administrators about the influence of patient demographic characteristics on clinical decision-making. Future research is needed to better understand these relationships, with the ultimate goal of improving patient care.

The Interaction of Patient Race, Provider Bias, and Clinical Ambiguity on Pain Management Decisions

“…Consistent with previous lens model studies examining pain decision-making [30,31,36], we examined the results of individual-level analyses at both p < .05 and p < .10. The significance level for all group-level analyses was set to p < .05.…”

The impact of patients’ gender, race, and age on health care professionals’ pain management decisions: An online survey using virtual human technology

“…Research has found that there are pain assessment and treatment disparities depending on the patient’s sex, race, and age (Alqudah, et al, 2010; Anderson et al, 2000; Breuer, 2010; Hirsh et al, 2009a; Hirsh et al, 2011). Women and minorities are less likely to receive opioid analgesics than their demographic counterparts, post-surgery, while in emergency rooms, and for cancer pain (Calderone, 1990; Chen & et al, 2008; Cleeland et al, 1994; Faherty & Grier, 1984).…”

Differentiation of bites of venomous snakes in Taiwan using the lens model approach

“…Finally, the lens model approach could be applied to other decision-making processes in the hospitals. Despite assessing pain judgment [16] and diagnosing psychiatric patients [31] introduced in the literature section, the method could be tested for discriminating food poisoning, differentiating chemical toxicity, or diagnosing ST segment elevation myocardial infraction (STEMI) patients.…”

“…For example, Hirsh et al [16] used the lens model approach to assess how medical staff use patient characteristics and facial expressions to make pain judgment. Speroff et al [26] used a number of cues and clinical data to build a lens model that conferred the relationship between clinical data and physiological measures of hemodynamic status.…”