Literature DB >> 31045605

Implicit Bias and the Feedback Paradox: Exploring How Health Professionals Engage With Feedback While Questioning Its Credibility.

Javeed Sukhera1, Michael Wodzinski, Alexandra Milne, Pim W Teunissen, Lorelei Lingard, Chris Watling.   

Abstract

PURPOSE: Learners and practicing health professionals may dismiss emotionally charged feedback related to self, yet little research has examined how to address feedback that threatens an individual's identity. The implicit association test (IAT) provides feedback to individuals regarding their implicit biases. Anticipating feedback about implicit bias might be emotionally charged for mental health professionals, this study explored their experience of taking the IAT and receiving their results, to better understand the challenges of identity-threatening feedback.
METHOD: The researchers sampled 32 psychiatry nurses, psychiatrists, and psychiatric residents at Western University in Ontario, Canada, after they completed the mental illness IAT and received their results. Using constructivist grounded theory, semistructured interviews were conducted from April to October 2017 regarding participants' experience of taking the IAT. Using constant comparative analysis, transcripts were iteratively coded and analyzed for results.
RESULTS: While most participants critiqued the IAT and questioned its credibility, many also described the experience of receiving feedback about their implicit biases as positive or neutral. Most justified their implicit biases while acknowledging the need to better manage them.
CONCLUSIONS: These findings highlight a feedback paradox, calling into question assumptions regarding self-related feedback. Participants' reactions to the IAT suggest that potentially threatening self-related feedback may still be useful to participants who question its credibility. Further exploration of how the feedback conversation influences engagement with self-related feedback is needed.

Entities:  

Year:  2019        PMID: 31045605     DOI: 10.1097/ACM.0000000000002782

Source DB:  PubMed          Journal:  Acad Med        ISSN: 1040-2446            Impact factor:   6.893


  9 in total

1.  Implicit bias in healthcare: clinical practice, research and decision making.

Authors:  Dipesh P Gopal; Ula Chetty; Patrick O'Donnell; Camille Gajria; Jodie Blackadder-Weinstein
Journal:  Future Healthc J       Date:  2021-03

2.  Battling Bias in Primary Care Encounters: Informatics Designs to Support Clinicians.

Authors:  Lisa G Dirks; Erin Beneteau; Janice Sabin; Wanda Pratt; Cezanne Lane; Emily Bascom; Reggie Casanova-Perez; Naba Rizvi; Nadir Weibel; Andrea L Hartzler
Journal:  Ext Abstr Hum Factors Computing Syst       Date:  2022-04-28

3.  Qualitative analysis of medical student reflections on the implicit association test.

Authors:  Cristina M Gonzalez; Yuliana S Noah; Nereida Correa; Heather Archer-Dyer; Jacqueline Weingarten-Arams; Javeed Sukhera
Journal:  Med Educ       Date:  2021-02-24       Impact factor: 7.647

4.  "I Didn't Really Have a Choice": Qualitative Analysis of Racial-Ethnic Disparities in Diabetes Technology Use Among Young Adults with Type 1 Diabetes.

Authors:  Shivani Agarwal; Gladys Crespo-Ramos; Judith A Long; Victoria A Miller
Journal:  Diabetes Technol Ther       Date:  2021-09       Impact factor: 7.337

Review 5.  Emotion as reflexive practice: A new discourse for feedback practice and research.

Authors:  Rola Ajjawi; Rebecca E Olson; Nancy McNaughton
Journal:  Med Educ       Date:  2021-11-25       Impact factor: 7.647

6.  Solutions to Address Inequity in Diabetes Technology Use in Type 1 Diabetes: Results from Multidisciplinary Stakeholder Co-creation Workshops.

Authors:  Shivani Agarwal; Gladys Crespo-Ramos; Stephanie L Leung; Molly Finnan; Tina Park; Katie McCurdy; Jeffrey S Gonzalez; Judith A Long
Journal:  Diabetes Technol Ther       Date:  2022-06       Impact factor: 7.337

7.  Pre-clinical medical student reflections on implicit bias: Implications for learning and teaching.

Authors:  Christine Motzkus; Racquel J Wells; Xingyue Wang; Sonia Chimienti; Deborah Plummer; Janice Sabin; Jeroan Allison; Suzanne Cashman
Journal:  PLoS One       Date:  2019-11-15       Impact factor: 3.240

8.  Leveraging Machine Learning to Understand How Emotions Influence Equity Related Education: Quasi-Experimental Study.

Authors:  Javeed Sukhera; Hasan Ahmed
Journal:  JMIR Med Educ       Date:  2022-03-30

9.  Implicit Bias Recognition and Management in Interpersonal Encounters and the Learning Environment: A Skills-Based Curriculum for Medical Students.

Authors:  Cristina M Gonzalez; Sydney A Walker; Natalia Rodriguez; Yuliana S Noah; Paul R Marantz
Journal:  MedEdPORTAL       Date:  2021-07-13
  9 in total

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