Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

94.7K
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
94.7K
Confirmation Biases01:31

Confirmation Biases

7.6K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
7.6K
Stereotype Content Model02:16

Stereotype Content Model

15.3K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.3K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.2K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.2K
Surveys02:16

Surveys

16.6K
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
16.6K
Obedience01:08

Obedience

35.1K
According to obedience research, we may harm others under the forceful pressures of an authority figure (Milgram, 1974). How about if the inappropriate orders were delivered with less force? The increasing interdependence between nurses and physicians compelled Hofling and his colleagues to explore nurses’ reactions to a potentially harmful medical request made by the perceived authority figure, the doctor (Hofling, Brotzman, Dalrymple, Graves, & Pierce, 1966). In this situation,...
35.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Neurodivergence in Medical Education: Current Landscape and Inclusive Future for Pediatrics.

Pediatric annals·2026
Same author

Gender discrimination and personal and professional development fostered by allopathic medical schools in the United States.

PloS one·2026
Same author

Mo<sub>2</sub>CT <sub><i>x</i></sub> MXene-based non-enzymatic electrochemical sensor for selective detection of hydrogen peroxide in colorectal cancer cells.

Nanoscale advances·2026
Same author

Medical School Cohorts and Preparedness to Work With Individuals From Different Backgrounds: A Cross-Sectional Study.

Health science reports·2026
Same author

Trends in National Institutes of Health Investigators by Sex, Race, Ethnicity, and Disability Status.

JAMA·2026
Same author

Self-Reported Patient-Perpetrated Sexual Harassment Among the US Urological Workforce.

JAMA network open·2026
Same journal

Personalizing N-of-1 Trial Results to Facilitate Decision-Making.

JAMA network open·2026
Same journal

Prostate Cancer Screening-Where We've Been, Where We Are, and What Comes Next.

JAMA network open·2026
Same journal

Comparison of Care Cascade Outcome Measures for Hepatitis C Among Insured US Adults.

JAMA network open·2026
Same journal

At-Home Transvaginal Pelvic Ultrasonography and Image Quality in Premenopausal Women: A Nonrandomized Clinical Trial.

JAMA network open·2026
Same journal

Aerobic Exercise and Subthreshold Depressive Symptoms in Adolescents: Secondary Analysis of a Randomized Clinical Trial.

JAMA network open·2026
Same journal

Prefrontal Transcranial Pulse Stimulation for Major Depressive Disorder: A Randomized Clinical Trial.

JAMA network open·2026
See all related articles

Related Experiment Video

Updated: Jan 6, 2026

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
09:55

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology

Published on: September 28, 2022

2.0K

Discrimination Experiences Among Medical Students.

Mytien Nguyen1, Shruthi Venkataraman2, Gabriel Abrams3

  • 1Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut.

JAMA Network Open
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

Medical students with disabilities (MSWD) face higher rates of general, gender-based, and race-based discrimination. Intersectional analysis reveals heightened discrimination risks for Asian, Black, and Hispanic female MSWD.

More Related Videos

Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity
07:32

Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity

Published on: February 10, 2016

9.8K
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.1K

Related Experiment Videos

Last Updated: Jan 6, 2026

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
09:55

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology

Published on: September 28, 2022

2.0K
Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity
07:32

Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity

Published on: February 10, 2016

9.8K
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.1K

Area of Science:

  • Medical Education
  • Health Disparities
  • Social Determinants of Health

Background:

  • Discrimination in medical school affects students based on race, ethnicity, sex, and sexual orientation.
  • Experiences of discrimination among medical students with disabilities and the impact of intersecting identities are understudied.

Purpose of the Study:

  • To investigate the association between disability status, sex, race, and ethnicity and experiences of general, gender-based, and race-based discrimination in medical school.
  • To explore the intersectional impact of these identities on discrimination.

Main Methods:

  • Cross-sectional study surveying graduating medical students from US accredited MD-granting institutions (2020-2022).
  • Modified Poisson regression was used to calculate relative risks for various discrimination types.
  • Analysis included disability status, sex, race, ethnicity, and their intersections.

Main Results:

  • Medical students with disabilities (MSWD) reported higher rates of general (RR, 1.57), gender-based (RR, 1.64), race-based (RR, 1.55), and multiple discrimination (RR, 1.82) compared to peers without disabilities.
  • Asian, Black, and Hispanic female MSWD experienced significantly higher risks of general and race-based discrimination compared to White male students without disabilities.
  • Specific intersectional groups, such as Asian and Black female MSWD, showed the highest relative risks for multiple types of discrimination.

Conclusions:

  • Disability status is independently associated with increased discrimination in medical school.
  • Intersectional identities, particularly for female students of color with disabilities, amplify experiences of discrimination.
  • Addressing intersecting forms of discrimination is crucial for supporting medical students with disabilities.