The DOI matches its Crossref record.
An educational intervention to improve women’s academic STEM outcomes: Divergent effects on well-represented vs. underrepresented minority women.
Open accessAbstract
Objectives The aim of this field experiment was to test the effect of a social psychological intervention on an ethnically diverse sample of first-year college women majoring in science, technology, engineering, and math (STEM). We hypothesized that grade point averages in STEM courses would be higher in the intervention condition relative to the control condition. Furthermore, we tested competing hypotheses about the moderating role of belonging to either a well-represented (WR) or underrepresented minority (URM) ethnic group in STEM. Method The sample (N = 199) included 115 women from WR ethnic groups and 84 women from URM ethnic groups who were randomly assigned to condition. Women in the intervention were educated about the harmful impact of gender stereotypes in STEM and provided with effective strategies for coping with stereotype threat. At the end of their first year, we obtained participants' academic transcripts. Results At the end of their first year in college, URM women in the intervention condition had higher grade point averages in their STEM courses than URM women in the control condition. The intervention had no effect on WR women. Conclusions The present research demonstrates the importance of intersectional approaches to studying the experiences of women in STEM. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Psychoeducation programs explaining the role of social identity threat and suggesting coping strategies have been found to weaken the negative effects of stereotype threat.
“Such programs have been found to weaken the negative effects of stereotype threat (O'Brien et al., 2019; see also Johns, Schmader, & Martens, 2005).”
From Tackling Educational Inequalities with Social Psychology: Identities, Contexts, and Interventions (2021) · cites this paper as O'Brien et al., 2019
Attributing underperformance to stereotype threat can benefit threatened students' performance.
“Attributing the underperformance to stereotype threat, however, can benefit threatened students' performance (Johns et al., 2005;O'Brien et al., 2019)”
From Tackling Educational Inequalities with Social Psychology: Identities, Contexts, and Interventions (2021) · cites this paper as O'Brien et al., 2019
Citations
Cited by
Sorted by citations
Tackling Educational Inequalities with Social Psychology: Identities, Contexts, and Interventions
Easterbrook, M. J., & Hadden, I. R. (2021) · Social Issues and Policy Review · doi:10.1111/sipr.12070 · Cited by 110
“threatened students’ performance (Johns et al., 2005; O’Brien et al., 2019), suggesting that being able to attribute historical underperformance to something other than the group’s ability is an effective way of dispelling the potential negative”
BackgroundCatalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity
Nixon, N., Lin, Y., & Snow, L. E. (2024) · Policy Insights from the Behavioral and Brain Sciences · doi:10.1177/23727322231220356 · Cited by 80
“For example, future work needs to be done surrounding intersectionality, accessibility including assistive technology for disability, and inclusion for neurodiversity (see also O ’ Brien et al., 2020).”
BackgroundStudent Attitudes and Interests in STEM in Qatar through the Lens of the Social Cognitive Theory
Sellami, A., Al-Ali, A., Allouh, A. M., & Alhazbi, S. (2023) · Sustainability · doi:10.3390/su15097504 · Cited by 27
“Extrinsic motivation comprises the perceived utility of mathematics, science, and technology for potential career paths [47], the expectations of parents and other important role models [48,49], and the desire to achieve good grades [50].”
BackgroundInterventions for gender equality in STEM education: A meta‐analysis
Yu, W., He, J., Luo, J., & Shu, X. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12928 · Cited by 30
Alam, M. S., Sajid, S., Kok, J. K., Rahman, M., & Amin, A. (2021) · Pertanika journal of social science & humanities · doi:10.47836/pjssh.29.2.06 · Cited by 26
Johnson, I. R., & Pietri, E. S. (2022) · Group Processes & Intergroup Relations · doi:10.1177/13684302221080467 · Cited by 23
“Future work should examine if making salient that women (of all racial identities) face gender bias in STEM (O’Brien et al., 2020) can serve as one potential context where White women can act as effective ally endorsers for Black women.”
BackgroundEverything changes but nothing changes: gender stereotypes in the Italian population
Silva, R. C., Vezzoli, M., Menesello, V., Meattini, M., Sartori, R., & Minelli, A. (2024) · Archives of Women's Mental Health · doi:10.1007/s00737-024-01437-1 · Cited by 13
Mohammed, T. F., Doud, N., Brownell, S. E., & Cooper, K. M. (2024) · Advances in Physiology Education · doi:10.1152/advan.00074.2024 · Cited by 8
Media representation and gender bias in science
Macdonald, C. (2021) · Journal of Environmental Media · doi:10.1386/jem_00036_1 · Cited by 6
Creative Choice in Fifth Grade Computing Curriculum
Mork, K., Wilcox, J., & Wood, Z. J. (2020) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3341525.3387405 · Cited by 3
Context matters: Diagnosing and targeting local barriers to success at school
Hadden, I. R., Harris, P. R., & Easterbrook, M. J. (2024) · Journal of School Psychology · doi:10.1016/j.jsp.2024.101401 · Cited by 3
Ober, T. M., Liu, C., & Cheng, Y. (2023) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000761 · Cited by 4
Introducing Computing to a Cohort of Incarcerated Youth
Mork, K., Migler, T., & Wood, Z. J. (2020) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3328778.3366820 · Cited by 3
Nurturing Women in STEM: A Multifaceted Approach for India
Saikia, M. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i05.34870 · Cited by 1
“Interventions that educate students about the impact of gender stereotypes can improve academic outcomes for underrepresented minority women in STEM, suggesting that awareness can mitigate bias [54].”
Using THRIVE as a Framework for Creating HBCU Success Stories
Mason, R. S., Byrd, C. D., & Muldrow, L. L. (2021) · doi:10.1108/978-1-80043-664-020211006 · Cited by 1
RESPECT 2020 Prepint proceedings
Burke, Q., Garcia, P., & Jacob, S. (2020) · Research in Equitable and Sustained Participation in Engineering, Computing, and Technology · doi:10.1109/respect49803.2020.9272473 · Cited by 1
“gov/statistics/srvydoctoratework/ [35] S.”
InfluentialMinority Status and Belonging: Engineering Math as a Vehicle to Build Community
Bielefeldt, A., Tsai, J. Y., Myers, B., Godrick, D., & Sullivan, J. (2020) · 2021 CoNECD Proceedings · doi:10.18260/1-2--36109 · Cited by 1
“This brings to the fore the notion of “multiple lenses” and intersectionality [20, 21].”
BackgroundLee, W. (2024) · Multicultural Perspectives · doi:10.1080/15210960.2024.2350074
“These social categories are deeply connected, creating unique challenges and barriers for individuals who fall within their intersection, especially in the STEM field (MacPhee et al., 2013; Mukherji et al., 2017; O ’ Brien et al., 2020).”
BackgroundThomas, H., Kamkwalala, A. R., Nath, A., McArthur, J. C., Wojna, V., Shiramizu, B. T., Sacktor, N. C., Pardo, C. A., Haughey, N. J., Clements, J. E., et al. (2023) · NeuroImmune Pharmacology and Therapeutics · doi:10.1515/nipt-2023-0009
Exploring Issues of Gender Equity in Girls' Out-of-School Time STEM Engagement
Upadhyay, P., Coleman, M., Bethune, A., Nacu, D. C., Erete, S., & Pinkard, N. (2020) · Research in Equitable and Sustained Participation in Engineering, Computing, and Technology · doi:10.1109/respect49803.2020.9272482
Metadata
- Authors
- O'Brien, L. T., Garcia, D. M., Blodorn, A., Adams, G., Hammer, E., & Gravelin, C.
- Affiliations
- California State University, San Bernardino (US)
- University of Kansas (US)
- Xavier University of Louisiana (US)
- SUNY Geneseo (US)
- Venue
- Cultural Diversity & Ethnic Minority Psychology
- Published
- 2020-04
- Volume
- 26
- Issue
- 2
- Pages
- 163-168
- ISSN
- 1077-341X (ISSN-L)
- 1939-0106
- 1099-9809
- OpenAlex
- W2942176773
- Cited by
- 23
- References
- 0
- Citations by year
- 2026: 1 · 2024: 6 · 2023: 6 · 2022: 2 · 2021: 3 · 2020: 4
- FWCI
- 4.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Cultural Diversity & Ethnic Minority Psychology (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (HRD0936722)
- Topics
- Social Sciences › Psychology › Applied Psychology › Career Development and Diversity
- Social Sciences › Psychology › Developmental and Educational Psychology › Education, Achievement, and Giftedness
- Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
- Keywords
- women in STEM, gender stereotypes, stereotype threat, social-psychological intervention, academic performance, grade point average, intersectionality, ethnic minority status, sense of belonging
- MeSH terms
- Educational Status, Engineering, Ethnicity, Female, Humans, Male, Mathematics, Minority Groups, Random Allocation, Stereotyping, Students, Technology, Universities, Cultural Diversity, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review