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Record W4390793257 · doi:10.1097/corr.0000000000002933

A Shadow of Doubt: Is There Implicit Bias Among Orthopaedic Surgery Faculty and Residents Regarding Race and Gender?

2024· article· en· W4390793257 on OpenAlexaboutno aff
Shawn R. Gilbert, Timothy Torrez, Achraf Jardaly, Kimberly Templeton, Gabriella E. Ode, Kelsie Coe, Joshua C. Patt, Mara L. Schenker, Gerald McGwin, Brent A. Ponce

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
FundersStryker
KeywordsImplicit-association testMedicineDiversity (politics)Race (biology)Test (biology)Implicit biasPrejudice (legal term)PopulationShadow (psychology)Orthopedic surgeryMEDLINEEthnic groupPsychological interventionSocial psychologyPsychologySurgeryNursing

Abstract

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BACKGROUND: Orthopaedic surgery continues to be one of the least diverse medical specialties. Recently, increasing emphasis has been placed on improving diversity in the medical field, which includes the need to better understand existing biases. Despite this, only about 6% of orthopaedic surgeons are women and 0.3% are Black. Addressing diversity, in part, requires a better understanding of existing biases. Most universities and residency programs have statements and policies against discrimination that seek to eliminate explicit biases. However, unconscious biases might negatively impact the selection, training, and career advancement of women and minorities who are underrepresented in orthopaedic surgery. Although this is difficult to measure, the Implicit Association Test (IAT) by Project Implicit might be useful to identify and measure levels of unconscious bias among orthopaedic surgeons, providing opportunities for additional interventions to improve diversity in this field. QUESTIONS/PURPOSES: (1) Do orthopaedic surgeons demonstrate implicit biases related to race and gender roles? (2) Are certain demographic characteristics (age, gender, race or ethnicity, or geographic location) or program characteristics (geographic location or size of program) associated with the presence of implicit biases? (3) Do the implicit biases of orthopaedic surgeons differ from those of other healthcare providers or the general population? METHODS: A cross-sectional study of implicit bias among orthopaedic surgeons was performed using the IAT from Project Implicit. The IAT is a computerized test that measures the time required to associate words or pictures with attributes, with faster or slower response times suggesting the ease or difficulty of associating the items. Although concerns have been raised recently about the validity and utility of the IAT, we believed it was the right study instrument to help identify the slight hesitation that can imply differences between inclusion and exclusion of a person. We used two IATs, one for Black and White race and one for gender, career, and family roles. We invited a consortium of researchers from United States and Canadian orthopaedic residency programs. Researchers at 34 programs agreed to distribute the invitation via email to their faculty, residents, and fellows for a total of 1484 invitees. Twenty-eight percent (419) of orthopaedic surgeons and trainees completed the survey. The respondents were 45% (186) residents, 55% (228) faculty, and one fellow. To evaluate response biases, the respondent population was compared with that of the American Academy of Orthopaedic Surgeons census. Responses were reported as D-scores based on response times for associations. D-scores were categorized as showing strong (≥ 0.65), moderate (≥ 0.35 to < 0.65), or slight (≥ 0.15 to < 0.35) associations. For a frame of reference, orthopaedic surgeons' mean IAT scores were compared with historical scores of other self-identified healthcare providers and that of the general population. Mean D-scores were analyzed with the Kruskal-Wallis test to determine whether demographic characteristics were associated with differences in D-scores. Bonferroni correction was applied, and p values less than 0.0056 were considered statistically significant. RESULTS: Overall, the mean IAT D-scores of orthopaedic surgeons indicated a slight preference for White people (0.29 ± 0.4) and a slight association of men with career (0.24 ± 0.3), with a normal distribution. Hence, most respondents' scores indicated slight preferences, but strong preferences for White race were noted in 27% (112 of 419) of respondents. There was a strong association of women with family and home and an association of men with work or career in 14% (60 of 419). These preferences generally did not correlate with the demographic, geographic, and program variables that were analyzed, except for a stronger association of women with family and home among women respondents. There were no differences in race IAT D-scores between orthopaedic surgeons and other healthcare providers and the general population. Gender-career IAT D-scores associating women with family and home were slightly lower among orthopaedic surgeons (0.24 ± 0.3) than among the general population (0.32 ± 0.4; p < 0.001) and other healthcare professionals (0.34 ± 0.4; p < 0.001). All of these values are in the slight preference range. CONCLUSION: Orthopaedic surgeons demonstrated slight preferences for White people, and there was a tendency to associate women with career and family on IATs, regardless of demographic and program characteristics, similar to others in healthcare and the general population. Given the similarity of scores with those in other, more diverse areas of medicine, unconscious biases alone do not explain the relative lack of diversity in orthopaedic surgery. CLINICAL RELEVANCE: Implicit biases only explain a small portion of the lack of progress in improving diversity, equity, inclusion, and belonging in our workforce and resolving healthcare disparities. Other causes including explicit biases, an unwelcoming culture, and perceptions of our specialty should be examined. Remedies including engagement of students and mentorship throughout training and early career should be sought.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.110
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.005
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.275
GPT teacher head0.468
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2024
Admission routes1
Has abstractyes

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