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

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

Bibliographic record

VenueThe Hearing Journal · 2021
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsHealth carePsychologyEthnic groupSuicidal ideationAnxietyDistressSexual orientationHealth equityClinical psychologyMedicinePsychiatrySocial psychologySuicide preventionNursingPoison controlPublic health

Abstract

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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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.411
Teacher spread0.329 · 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 teacher head, not a consensus.

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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Citations1
Published2021
Admission routes1
Has abstractyes

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