Identifying barriers to integrated and equitable care for older adults from minority groups leading to potentially avoidable emergency department visits.
Bibliographic record
Abstract
Introduction: Emergency departments (ED) around the world are overcrowded and wait times are long. Many ED visits could be avoided if patients’ needs could be met in a more appropriate care setting. Yet, patients, especially older adults from minority linguistic and ethno-cultural groups, continue to experience multiple and intersecting systemic and structural barriers to accessing equitable and culturally safe care. Our team has shown that when patients and their health care providers in the hospital do not speak the same first language, it is associated with increased health system use and poor health outcomes for patients1. This study argues for movement towards a more integrated, interprofessional care approach to the management and delivery of more culturally informed and linguistically appropriate health care as a means of reducing potentially avoidable ED use. Approach: In collaboration with patient and caregiver partners and ED staff, we collected data on patients’ primary care use and perceived barriers to care. In 2023, we recruited older adults (65+) (n=90) and caregivers (n=10) during a visit to an ED in two Canadian provinces. Using statistical and reflexive thematic analyses, we triangulated data from surveys, short answer questions, and chart review to identify a subset of older adults who (1) self-identified as belonging to a linguistic and/or ethno-cultural minority group and (2) had experienced a visit to the ED that was classified as “potentially avoidable” using a tool we co-developed with ED physicians. We are now conducting in-depth interviews with older adults (n=10) to better understand their care experiences using an intersectionality framework. Results: Our findings to date have indicated that when older adults experience difficulties accessing a family physician who can provide timely care, they visit an ED instead. They also do not perceive having the choice to seek out linguistic and/or culturally safe care because of the lack of availability of suitable care providers in the community. We also heard about perceived patient-care provider power imbalances involved in the current processes to request and use a translator during health care provision. Patients suggested that there is not yet a good solution to deal with cultural discordance in care provision but viewed the expansion of interprofessional practice in the ED and better integration of community-based primary care with resources and support for cultural and linguistic minorities as promising ways forward. International Learnings: This project highlights both gaps and promising approaches in the delivery of linguistically appropriate and culturally safe care for older adults, highlighting the need for more integrated and interprofessional approaches to management and service delivery. Next steps include work to amplify the voices of older adult patients and caregivers through co-design work to develop recommendations for improvements to service delivery in the ED and community that will be pilot tested and evaluated with community partners and care providers in a bilingual Canadian hospital. 1 Seale E, et al. Patient–physician language concordance and quality and safety outcomes among frail home care recipients admitted to hospital in Ontario, Canada. CMAJ 2022 Jul; 194(26): E899-E908; DOI: 10.1503/cmaj.212155.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".