Racial Microaggressions in Healthcare Settings: A Scoping Review
Notice bibliographique
Résumé
Aims Racial microaggressions occur when subtle or often automatic exchanges of aversive and covert racism are directed towards people identifying as racialized groups. Consequently, affecting individuals' mental and physical health. Healthcare professionals are a vulnerable group to the effects of racial microaggressions, given the high prevalence of burnout. The aim of the review was to explore healthcare professionals and students' experience of racial microaggressions in healthcare settings Methods A PROSPERO registered scoping review was conducted using the PRISMA extension for scoping review guidelines. The literature search was undertaken in August 2020, of five databases, MEDLINE, EMBASE, CINAHL, PsycINFO, EMCARE and we also searched the ‘grey literature.’ Studies featuring primary data on racialized or migrant microaggressions towards professionals or students in healthcare settings were included. We excluded studies that were not in English. QDA Miner was used to analyse the data, using a non-essentialist perspective, which suggests that ‘culture’ is a movable concept used by different people at different times to suit purposes of identity, politics and science. Results Our search identified 8 papers (5 qualitative, 2 mixed and 1 quantitative) on the experience of microaggressions towards healthcare professionals and students (n = 602). Almost all (87.5%) were conducted in North America and only one (12.5%) in the UK. The primary themes were as follows: Intersectionality: Individual and group social categorizations of race, class, and gender were described as interconnected, leading to interdependent systems of discrimination or disadvantage. Healthcare professionals indicated that increasing diversity and racial representation can reduce bias and thus microaggressions among stakeholders in the culture of work. Workplace culture and lack of senior support: The healthcare curriculum, and the manner of its delivery were found to propagate ideas encouraging racial microaggressions. Seniors behaving as role-models by challenging microaggressions could encourage an open and accountable environment. Supervision was a tool for allyship that reduced the threat of negative race-related incidents. Intervention: Acknowledging racial microaggressions within healthcare, as well as quantifying their presence with tools, encouraged a stronger and more effective response from institutions. Teaching curriculum also served as a useful platform to teach and address microaggressions. Conclusion Racial microaggressions were experienced as having a detrimental impact on healthcare professionals’ well-being and mental health. Consequently, this affected the efficiency, the workplace culture, patient outcomes and job satisfaction. Given the multifaceted nature of racial microaggressions, tackling them requires a complex and wide-ranging response from institutions.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,023 | 0,026 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».