Literature DB >> 25124965

Using virtual human technology to provide immediate feedback about participants' use of demographic cues and knowledge of their cue use.

Laura D Wandner1, Janelle E Letzen1, Calia A Torres2, Benjamin Lok3, Michael E Robinson4.   

Abstract

UNLABELLED: Demographic characteristics have been found to influence pain management decisions, but limited focus has been placed on participants' reactions to feedback about their use of sex, race, or age to make these decisions. The present study aimed to examine the effects of providing feedback about the use of demographic cues to participants making pain management decisions. Participants (N = 107) viewed 32 virtual human patients with standardized levels of pain and provided ratings for virtual humans' pain intensity and their treatment decisions. Real-time lens model idiographic analyses determined participants' decision policies based on cues used. Participants were subsequently informed about cue use and completed feedback questions. Frequency analyses were conducted on responses to these questions. Between 7.4 and 89.4% of participants indicated awareness of their use of demographic or pain expression cues. Of those individuals, 26.9 to 55.5% believed this awareness would change their future clinical decisions, and 66.6 to 75.9% endorsed that their attitudes affect their imagined clinical practice. Between 66.6 and 79.1% of participants who used cues reported willingness to complete an online tutorial about pain across demographic groups. This study was novel because it provided participants feedback about their cue use. Most participants who used cues indicated willingness to participate in an online intervention, suggesting this technology's utility for modifying biases. PERSPECTIVE: This is the first study to make individuals aware of whether a virtual human's sex, race, or age influences their decision making. Findings suggest that a majority of the individuals who were made aware of their use of demographic cues would be willing to participate in an online intervention.
Copyright © 2014 American Pain Society. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Virtual human technology; age; cues; feedback; race; sex

Mesh:

Year:  2014        PMID: 25124965      PMCID: PMC4252619          DOI: 10.1016/j.jpain.2014.08.001

Source DB:  PubMed          Journal:  J Pain        ISSN: 1526-5900            Impact factor:   5.820


  15 in total

1.  Investigating patient characteristics on pain assessment using virtual human technology.

Authors:  Lauren A Stutts; Adam T Hirsh; Steven Z George; Michael E Robinson
Journal:  Eur J Pain       Date:  2010-11       Impact factor: 3.931

2.  SEX AND RACE DIFFERENCES IN RATING OTHERS' PAIN, PAIN-RELATED NEGATIVE MOOD, PAIN COPING, AND RECOMMENDING MEDICAL HELP.

Authors:  Ashraf F Alqudah; Adam T Hirsh; Lauren A Stutts; Cindy D Scipio; Michael E Robinson
Journal:  J Cyber Ther Rehabil       Date:  2010

3.  Judgments about pain intensity and pain genuineness: the role of pain behavior and judgmental heuristics.

Authors:  Marc O Martel; Pascal Thibault; Michael J L Sullivan
Journal:  J Pain       Date:  2011-02-05       Impact factor: 5.820

4.  Patient demographic characteristics and facial expressions influence nurses' assessment of mood in the context of pain: a virtual human and lens model investigation.

Authors:  Adam T Hirsh; Sarah B Callander; Michael E Robinson
Journal:  Int J Nurs Stud       Date:  2011-05-19       Impact factor: 5.837

5.  National Ambulatory Medical Care Survey: 2004 summary.

Authors:  Esther Hing; Donald K Cherry; David A Woodwell
Journal:  Adv Data       Date:  2006-06-23

Review 6.  Pain assessment and management in persons with dementia.

Authors:  Ann L Horgas; Amanda Floetke Elliott
Journal:  Nurs Clin North Am       Date:  2004-09       Impact factor: 1.208

7.  Using virtual human technology to capture dentists' decision policies about pain.

Authors:  L D Wandner; A T Hirsh; C A Torres; B C Lok; C D Scipio; M W Heft; M E Robinson
Journal:  J Dent Res       Date:  2013-02-27       Impact factor: 6.116

8.  The consistency of facial expressions of pain: a comparison across modalities.

Authors:  Kenneth M Prkachin
Journal:  Pain       Date:  1992-12       Impact factor: 6.961

9.  Virtual human technology: patient demographics and healthcare training factors in pain observation and treatment recommendations.

Authors:  Laura D Wandner; Lauren A Stutts; Ashraf F Alqudah; Jason G Craggs; Cindy D Scipio; Adam T Hirsh; Michael E Robinson
Journal:  J Pain Res       Date:  2010-12-07       Impact factor: 3.133

10.  Virtual human technology: capturing sex, race, and age influences in individual pain decision policies.

Authors:  Adam T Hirsh; Ashraf F Alqudah; Lauren A Stutts; Michael E Robinson
Journal:  Pain       Date:  2008-10-18       Impact factor: 7.926

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  2 in total

1.  Ethnic Differences in Experimental Pain Responses Following a Paired Verbal Suggestion With Saline Infusion: A Quasiexperimental Study.

Authors:  Janelle E Letzen; Troy C Dildine; Chung Jung Mun; Luana Colloca; Stephen Bruehl; Claudia M Campbell
Journal:  Ann Behav Med       Date:  2021-02-12

2.  A randomized controlled trial testing a virtual perspective-taking intervention to reduce race and socioeconomic status disparities in pain care.

Authors:  Adam T Hirsh; Megan M Miller; Nicole A Hollingshead; Tracy Anastas; Stephanie T Carnell; Benjamin C Lok; Chenghao Chu; Ying Zhang; Michael E Robinson; Kurt Kroenke; Leslie Ashburn-Nardo
Journal:  Pain       Date:  2019-10       Impact factor: 7.926

  2 in total

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