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Twelve Principles Trainees, PIs, Departments, and Faculties Can Use to Reduce Bias and Discrimination in STEM.

ABSTRACT: There is an overwhelming amount of evidence demonstrating that people from marginalized groups, including women, racialized and Indigenous peoples, people with disabilities, immigrants, and LGBTQ+ individuals, continue to face substantial discrimination in STEM, manifested as both overt bias and unconscious bias. These biases result in discrimination against individuals in marginalized groups, and independent biases collectively contribute to a culture that systematically discriminates against people from marginalized groups. Representation from marginalized groups in postsecondary degrees in natural science and engineering has not substantially improved in over a decade. A set of 10 concrete principles are presented that trainees, principle investigators, departments, and faculties can use to enhance the participation and lived experiences of people in marginalized groups in STEM.

SUBMITTER: Willis LM

PROVIDER: S-EPMC7760482 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Twelve Principles Trainees, PIs, Departments, and Faculties Can Use to Reduce Bias and Discrimination in STEM.

Willis Lisa M LM Mehta Devang D Davis Alexandra A

ACS central science 20201203 12

There is an overwhelming amount of evidence demonstrating that people from marginalized groups, including women, racialized and Indigenous peoples, people with disabilities, immigrants, and LGBTQ+ individuals, continue to face substantial discrimination in STEM, manifested as both overt bias and unconscious bias. These biases result in discrimination against individuals in marginalized groups, and independent biases collectively contribute to a culture that systematically discriminates against p ...[more]

PMID: 33376790

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Project description:Compared to many other forms of social bias, weight bias is pervasive, socially accepted, and difficult to attenuate. According to the common ingroup identity model, strategies that expand group inclusiveness may promote more positive intergroup attitudes and behaviors, particularly when people are aware of unjust treatment of others included within their shared identity. Considering that most people are not aware of the social justice issue of weight discrimination, we hypothesized that a common ingroup identity would be effective in reducing weight bias primarily when unfair weight-based treatment was made salient (i.e., that fat people experience discrimination in employment). Participants were randomly assigned to conditions following a 3 (discrimination salience: weight discrimination, height discrimination, control) × 2 (group identity: common ingroup, control) design and completed an evaluative measure of weight bias. Results revealed a significant interaction, showing that when weight discrimination was salient, participants in the common ingroup identity condition reported less weight bias than participants in the group identity control condition. When a common ingroup identity was emphasized, weight bias was lower when weight discrimination was salient compared to when height discrimination was salient and the control condition in which nothing about discrimination was mentioned. These results were not moderated by participant weight. This study demonstrates that a common ingroup identity can be effective in reducing weight bias if a cue is provided that fat people experience disparate and unjust outcomes in employment. Given the serious consequences of weight bias for health and well-being, and the relative ease of implementing this prejudice-reduction intervention, the common ingroup identity model has potential application for reducing weight bias in a range of real-world settings. However, these findings should be considered preliminary until they are replicated in well-powered and pre-registered future research.

Dataset Information

Twelve Principles Trainees, PIs, Departments, and Faculties Can Use to Reduce Bias and Discrimination in STEM.

Publications

Twelve Principles Trainees, PIs, Departments, and Faculties Can Use to Reduce Bias and Discrimination in STEM.

Similar Datasets

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