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Enregistrement 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 sur 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

Notice bibliographique

RevueClinical Orthopaedics and Related Research · 2024
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueDiversity and Career in Medicine
Établissements canadiensnon disponible
Organismes subventionnairesStryker
Mots-clésImplicit-association testMedicineDiversity (politics)Race (biology)Test (biology)Implicit biasPrejudice (legal term)PopulationShadow (psychology)Orthopedic surgeryMEDLINEEthnic groupPsychological interventionSocial psychologyPsychologySurgeryNursing

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,020
score de la tête « metaresearch » (Gemma)0,110
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,105

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0200,110
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,005
Communication savante0,0020,004
Science ouverte0,0010,003
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,275
Tête enseignante GPT0,468
Écart entre enseignants0,193 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2024
Routes d'admission1
Résumé présentoui

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