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Record W4296185270 · doi:10.1002/emp2.12805

Invited Editorial: Patient perspectives of the climate of diversity, equity, and inclusion in the emergency department

2022· editorial· en· W4296185270 on OpenAlexaff
Juan A. March, Sing‐Yi Feng, Elizabeth Donnelly

Bibliographic record

VenueJournal of the American College of Emergency Physicians Open · 2022
Typeeditorial
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsEquity (law)Likert scaleEmergency departmentHealth careDescriptive statisticsCensusInclusion (mineral)PopulationPsychologyFamily medicineMedicineNursingSocial psychologyPolitical scienceStatistics

Abstract

fetched live from OpenAlex

As the "safety net" for US healthcare, emergency departments (EDs) provide critical healthcare access to a diverse group of patients regardless of insurance coverage or ability to pay.1 Yet, ED clinicians' implicit biases, which are similar to the levels in the general population, can influence their clinical decision making and the patients' perception of care.2 Because perception is reality, a patient's perception of their healthcare professional has a direct effect on medication compliance, willingness to communicate, and treatment adherence.2-6 Unfortunately, there is no published data on patient's perceptions of diversity, equity, and inclusion (DEI) in the ED. For these reasons, the study by Davuluri et al and its results are important. The cross-sectional survey by Davuluri et al describes patients' perspectives of the climate of DEI in the ED.1 The survey was developed by an interdisciplinary group of physicians, nurses, social workers, and so on. The survey consisted of 41 questions, largely organized into matrices and divided into the following 4 sections: care for specific patient populations, patient's care experiences and values, ED compared to other hospital system clinics and departments, and demographics. To minimize acquiesce bias, the authors intentionally combined positive and negative valence questions using a 5-point Likert-type scale. Analyses included descriptive statistics for all variables and continuous data as means with standard deviations and categorical data as counts and percentages. The survey was administered in a single urban ED with an annual census of 100,000 representing >60,000 unique patients. Of 1691 patients screened, 849 respondents were sampled. Demographics of the local population were similar to the survey results with 4.3% Latino/Hispanic, 10.1% White non-Hispanic, and 72.9% Black non-Hispanic. Only 0.82% (n = 7) of the surveys were completed in Spanish. The investigators found that most respondents reported that ED staff treated patients from all races equally and made patients feel accepted. Respondents identified that the ED staff's treatment of patients who are mentally ill (16.8%) or lower income (14.3%) as needing the most improvement. This is noteworthy because patients who were mentally ill were not surveyed, and this observation was made by other patients. A high percentage of patients (16.8%) in the study witnessed discrimination or harassment of ED staff by another patient. Harassment of healthcare professionals and ED staff is not a new finding, but to realize that patients are witnessing and perhaps being impacted by these events at such a high percentage is concerning.7-9 This finding illustrates a dynamic that is underexplored in EDs: how witnessed interactions between others may impact a patients' perception of their own care and may warrant further exploration. This study had numerous limitations, including convenience sampling and a novel survey administered in only 1 urban ED with a high percentage of underrepresented minorities. The authors did not comment on the percentage of ED staff who were also underrepresented minorities, and this may have affected the results. Although the survey was translated into Spanish, the research assistants did not speak Spanish, and this may have excluded a percentage of possible Hispanic respondents. Despite limitations, this study is one of the first to study patients' perception of DEI in the ED. The old saying that perception is reality holds true, and thus patients' perceptions of DEI provide important insights that could help guide strategic initiatives to improving future healthcare for subgroups of ED patients. This study could serve as part of a model for the ongoing assessment in other EDs. As patient populations are fluid, these perceptions may change over time, and as such, an approach to measurement that includes patient perceptions may allow for a broad understanding of the ways in which DEI is perceived and where there may be room for improvement. Another strength in this study is the acknowledgment by the authors that these patient/professional interactions are occurring in the context of systemic pressures, both within the context of the global structural oppression of minorities in Western society and within the structural pressure of an overburdened ED system. Systems that are dealing with pressures such as overcrowding force providers to deliver care in deeply suboptimal ways. Acknowledging the impact of the environment and how it may influence these patient experiences is important as it frames these issues within the context in which they are happening and not solely as dependent on individual interactions. Clearly, future studies are needed to further examine patients' perceptions of DEI in suburban, rural, and other urban EDs and those with different patient demographics. Future research should include an assessment of the consequences of institutional stigma in EDs, including variations in patient outcomes according to various demographic indicators of DEI.

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)0.004
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.023
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0050.003
Open science0.0050.001
Research integrity0.0140.011
Insufficient payload (model declined to judge)0.0240.009

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 head0.020
GPT teacher head0.335
Teacher spread0.315 · 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 designNot applicable
Domainnot available
GenreEditorial

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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Citations0
Published2022
Admission routes1
Has abstractyes

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