scispace - formally typeset
Open AccessJournal ArticleDOI

Implicit and explicit anti-fat bias among a large sample of medical doctors by BMI, race/ethnicity and gender

TLDR
It is concluded that strong implicit and explicit anti-fat bias is as pervasive among MDs as it is among the general public.
Abstract
Overweight patients report weight discrimination in health care settings and subsequent avoidance of routine preventive health care. The purpose of this study was to examine implicit and explicit attitudes about weight among a large group of medical doctors (MDs) to determine the pervasiveness of negative attitudes about weight among MDs. Test-takers voluntarily accessed a public Web site, known as Project Implicit®, and opted to complete the Weight Implicit Association Test (IAT) (N = 359,261). A sub-sample identified their highest level of education as MD (N = 2,284). Among the MDs, 55% were female, 78% reported their race as white, and 62% had a normal range BMI. This large sample of test-takers showed strong implicit anti-fat bias (Cohen's d = 1.0). MDs, on average, also showed strong implicit anti-fat bias (Cohen's d = 0.93). All test-takers and the MD sub-sample reported a strong preference for thin people rather than fat people or a strong explicit anti-fat bias. We conclude that strong implicit and explicit anti-fat bias is as pervasive among MDs as it is among the general public. An important area for future research is to investigate the association between providers' implicit and explicit attitudes about weight, patient reports of weight discrimination in health care, and quality of care delivered to overweight patients.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Implicit bias in healthcare professionals: a systematic review

TL;DR: The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population, and the need for the healthcare profession to address the role of implicit biases in disparities in healthcare is highlighted.
Journal ArticleDOI

Joint international consensus statement for ending stigma of obesity.

Francesco Rubino, +40 more
- 04 Mar 2020 - 
TL;DR: In this paper, a multidisciplinary group of international experts, including representatives of scientific organizations, reviewed available evidence on the causes and harms of weight stigma and, using a modified Delphi process, developed a joint consensus statement with recommendations to eliminate weight bias.
Journal ArticleDOI

Weight discrimination and bullying.

TL;DR: The nature and extent of weight stigmatization against overweight and obese individuals, as well as the resulting consequences that these experiences create for social, psychological, and physical health for children and adults who are targeted, are summarized.
Journal ArticleDOI

Stigma Experienced by Children and Adolescents With Obesity

TL;DR: This policy statement seeks to raise awareness regarding the prevalence and negative effects of weight stigma on pediatric patients and their families and provides 6 clinical practice and 4 advocacy recommendations regarding the role of pediatricians in addressing weight stigma.
Journal ArticleDOI

How and why weight stigma drives the obesity 'epidemic' and harms health.

TL;DR: Weight stigma is likely to drive weight gain and poor health and thus should be eradicated, which can begin by training compassionate and knowledgeable healthcare providers who will deliver better care and ultimately lessen the negative effects of weight stigma.
References
More filters
Book

Statistical Power Analysis for the Behavioral Sciences

TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Journal ArticleDOI

Measuring individual differences in implicit cognition: The implicit association test.

TL;DR: An implicit association test (IAT) measures differential association of 2 target concepts with an attribute when instructions oblige highly associated categories to share a response key, and performance is faster than when less associated categories share a key.
Journal ArticleDOI

Understanding and using the Implicit Association Test: I. An improved scoring algorithm.

TL;DR: The best-performing measure incorporates data from the IAT's practice trials, uses a metric that is calibrated by each respondent's latency variability, and includes a latency penalty for errors, and strongly outperforms the earlier (conventional) procedure.
Journal ArticleDOI

Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity.

TL;DR: A review of 122 research reports (184 independent samples, 14,900 subjects) found average r =.274 for prediction of behavioral, judgment, and physiological measures by Implicit Association Test (IAT) measures as mentioned in this paper.
Related Papers (5)