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Enregistrement W3148444769 · doi:10.1097/01.hj.0000743728.57095.dc

Serving Culturally and Linguistically Diverse Patients in Audiology-Part 1: Avoiding Microaggressions

2021· article· en· W3148444769 sur OpenAlexaboutno aff
Katie M. Colella, Erica Friedland, Laura Gaeta

Notice bibliographique

RevueThe Hearing Journal · 2021
Typearticle
Langueen
DomaineHealth Professions
ThématiqueInterpreting and Communication in Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHealth carePsychologyEthnic groupSuicidal ideationAnxietyDistressSexual orientationHealth equityClinical psychologyMedicinePsychiatrySocial psychologySuicide preventionNursingPoison controlPublic health

Résumé

récupéré en direct d'OpenAlex

Culturally and linguistically diverse (CLD) patients, those who do not speak English or of a non-dominant culture in the United States, are at higher risk of experiencing implicit bias from their health care providers. This results in lower patient satisfaction and poorer compliance with treatment recommendations.1Shutterstock/arloo, audiology, diversity, microagressions.Figure 1: Sample microaggressions and their implied messages. Audiology, diversity, microagressions.Table 1: Resources for Other Forms of Health Care Microaggressions. Audiology, diversity, microagressions.One form of bias is the use of microaggressions, which are subtle comments or actions that, intentionally or not, harm a non-dominant group, including patients of cultural and linguistic diversity.2 Microaggressions in health care are harmful, may diminish patient-provider communication, and do not align with patient- and family-centered care. Though often unintentional or unconsciously expressed, these comments are alienating and build barriers to successful patient care. Individuals can be marginalized, sometimes daily, based upon gender, age, sexual orientation, religion, race, ethnicity, body size, gender identity, ability, and citizenship status.3 The health care setting is a complicated landscape to navigate with microaggressions occurring in many forms. These include health care providers against patients, patients against health care providers, health care providers against trainees, patients against trainees, and between colleagues. Minor acts do not necessarily cause distress; however, minor acts accumulate over time resulting in a negative psychological effect. People who suffer chronic microaggressions display anxiety and low self-esteem. Additionally, they develop dysfunctional coping behaviors including depression, hypervigilance, skepticism, rage, anger, fatigue, and hopelessness.4 Although this article focuses on microaggressions from health care providers against patients, the basics of microaggressions are universal. These comments insult a non-dominant group yet mask themselves in ordinary conversation or banter. Figure 1 shows examples of microaggressions and the underlying discrimination it sends to CLD patients.2 INSIDE THE SOUNDBOOTH: AWARENESS OF MICROAGGRESSIONS As audiologists, we appreciate the contained and controlled environment of the sound booth. We can view the data clearly and start to understand the physiological implications. By taking control of the controllable, we can increase our awareness of microaggressions. To understand what causes microaggressions, let us unpack the basic psychology of what causes them. Anytime we meet anyone, our brain catalogs them based on our own perceived categories, such as gender, age, race, etc. Our brain flags if this person is in a different category than us. This is known as category activation. The brain registering these differences is unavoidable, but it is what our brain does next that matters most. Our brains can notice the difference and move on—something that happens all the time without us noticing. Or, our brain can fixate on it. When our brain lingers on the difference, due to not understanding it, not wanting to notice the difference, or reacting to an external comment, it generates anxiety. Anxiety coupled with category activation primes the fear center of the brain for stereotyping, even when our rational, conscious brain knows better.5 We can retrain our own category activation by acknowledging it. Observe what you notice each time you work with a patient. According to research about building successful habits, awareness and choosing to change are two crucial steps. It also helps to be specific with your intentions (i.e., “I will focus on my case history) and remember the why: We want to provide a clear, safe path for our patients’ hearing health.6 OUTSIDE OF THE SOUNDBOOTH: INTERRUPTING MICROAGGRESSIONS Once we start retraining our brains, we need to prepare to interrupt microaggressions when witnessed. This step is challenging because our controlled soundbooth of comfort may not always be representative or prepare us for real-world environments. But failure to act only contributes to negative feelings, like self-doubt or isolation, or can appear as an agreement with what was said. To create a safe and inclusive environment, be an ally when a microaggression is experienced. The University of Toronto Faculty of Medicine recommends the acronym C.A.R.E.S., which stands for: Consider how what one said was harmful, be Accountable for your actions and willing to apologize, Rethink harmful assumptions or stereotypes, Empathize with those on the receiving end of microaggressions, and Support by offering resources and asking how you can help.7 If you witness a microaggression, take the first step by acknowledging the comment or question. Pause and ask for clarification, such as “What did you mean when you said…?” As a witness, you can be an ally. Next, describe what you observed and how it impacted you.8 For example, you could say, “When you said…, it made me feel…” or “I feel X when you say Y because Z.”9 Although you might feel uncomfortable the first time, you will find that your confidence and effectiveness will improve over time. If you unintentionally express bias through a microaggression, admit that harm was caused. Reacting defensively invalidates someone's feelings and may cause a person to feel that they are responsible. Shrugging off the event as being “no big deal” is even worse as many victims are often accused of being hypersensitive. Explaining how your behavior will change should be part of the apology, and engaging in self-reflection is necessary so that future mistakes can be avoided. Below are some phrases that can be used to respond like an ally:10 “I recognize I have work to do.” “I'm going to take some time to reflect on this.” “I apologize. I'm going to do better.” “How can I make this right?” “What I'm gathering is [insert what you have learned].” “I believe you.” CLD patients deserve dignified care in a safe environment. By taking simple steps to build a stronger awareness of microaggression, we can prepare ourselves to intervene appropriately. After all, just because our soundbooths are quiet does not mean we should be when witnessing biased behavior towards our patients.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Intégrité de la recherche
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 enseignants0,044
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,082
Tête enseignante GPT0,411
Écart entre enseignants0,329 · 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 tête enseignante, pas un consensus.

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

Citations1
Publié2021
Routes d'admission1
Résumé présentoui

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