Program Summary:  This course explores explicit and implicit biases in health care and provides an overview of the terminology, the tools used to measure implicit bias, the evidence for implicit bias in health care, the impact of implicit bias in health care, the impact of interventions designed to reduce implicit bias, the key structural factors affecting implicit bias, and the relationship between implicit bias and structural inequities.  The course suggests that implicit bias interventions alone are not enough to reduce biased decision making and health disparities and should be accompanied by other systemic changes.

This course is recommended for social workers and health care professionals and is appropriate for beginning and intermediate levels of practice. Read the complete description of this course…..

Program Summary:  This course explores the potential harms of biased language in child welfare practice and offers strategies for reducing implicit or unconscious bias and improving practice.  The course examines key concepts to understanding language bias in child welfare, including neutral language, labels, the concrete-to abstract continuum, and sociolinguistic inequality.  Examples of non-stigmatizing and preferred terms are offered.

This course is recommended for social workers and counselors and is appropriate for beginning and intermediate levels of practice.  

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Program Summary:   This course examines stigmatizing language in the electronic health record and how stigmatizing words and phrases may impact health equity.   The course explores three forms of stigmatizing language: labeling someone as other, blaming, and invoking danger.  Patients with diabetes, substance use disorder, and chronic pain are explored as subgroups.  Findings and study limitations are presented.

This course is recommended for social workers, counselors, and therapists.

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Program Summary:   This course explores potential implicit bias in healthcare by looking at stigmatizing language in the healthcare record. The course highlights a study using machine learning to analyze electronic health records using 15 different patient descriptors:  (non-) adherent, aggressive, agitated, angry, challenging, combative, (non-)compliant, confront, (non-) cooperative, defensive, exaggerate, hysterical, (un-)pleasant, refuse, and resist.  A discussion of results and recommendations are given.

This course is recommended for social workers, counselors, and therapists.

Read the complete description of this course…..