This past Friday, several leaders at our institution participated in a seminar on implicit bias, taught (virtually, of course) by Dr. Uché Blackstock. As part of our training, we took the Harvard Implicit Association Test, a well-validated test that measures an individual's attitudes and beliefs about issues such as age, race, ethnicity, gender, and sexuality. Even if you have taken this test in the past, I encourage you to take another look, as they have added some new tests fairly recently.
I talked about implicit bias in my last post ("The 'Eat Lead Barbie' Story"). Implicit bias is commonly defined as an implicit (i.e. unconscious) stereotype, belief, or attitude about a member of a particular social group. For example, I may emphatically state that it's perfectly normal for men to express their emotions, but yet at the same time, my actions, consistent with and motivated by my unconscious biases and beliefs, reinforce the stereotype that "boys don't cry" (which, in full disclosure, is not what I believe at all). With regards to implicit bias, the adage that "knowledge if power" is certainly true. If we can uncover some of our implicit biases through tests like the Harvard Implicit Association Test, we can then explicitly do things to mitigate them!
As it turns out, we are all subject to implicit bias. A couple of interesting studies illustrate my point. First (Sex Differences: A Study of the Eye of the Beholder), two psychologists from Cornell University studied how 204 male and female subjects rated infants' emotional responses to four different stimuli (a teddy bear, a jack-in-the-box, a doll, and a buzzer). Half of the subjects were told that they were observing a "boy", while the other half were told that they were observing a "girl" (which may not have been true - that was part of the experiment!). The same infant's emotional response to one of the stimuli was rated very differently, depending upon whether the study subjects were told that the infant was a male or a female. For example, the jack-in-the-box stimulus frequently made the infants cry. "Female" infants were more often rated as displaying "fear" for their emotional response, whereas "male" infants were rated as displaying "anger". As the investigators conclude that "differences in the attributed emotional response were in the eye of the beholder".
Here's another fascinating study (The relationship between physician/nurse gender and patients' correct identification of health care professional roles), though not too surprising, given what I hear from female colleagues. A random selection of 150 patients who had received treatment in the emergency department were asked to identify the gender of each member on their treatment team (physician, nurse, allied health professional, etc). Patients correctly recognized male emergency medicine physicians as physicians 75% of the time, while they only correctly recognized female emergency medicine physicians as physicians 58% of the time (the difference was statistically significant). In contrast, patients correctly recognized male nurses as nurses 77% of the time versus 99% of the time with female nurses. Here is a great example of implicit bias based upon the stereotype that nurses are more often female and physicians are more often male.
It's important to recognize our own implicit biases. The problem is that these biases often lie deep in our subconscious and only come to the surface when it is too late. That is why I think it is important to learn about your own implicit biases through tests such as the Harvard Implicit Association Test.
The late Nelson Mandela said, "No one is born hating another person because of the color of his skin, or his background, or his religion. People must learn to hate, and if they can learn to hate, they can be taught to love, for love comes more naturally to the human heart than its opposite."
We can all learn to love. But it starts with uncovering our own implicit biases.
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