Lived experiences of palliative care physicians on the impacts of language and cultural discordance on end-of-life care across Ontario, Canada: a qualitative study using the intersectionality-based policy framework
Bibliographic record
Abstract
BACKGROUND: Language and cultural discordance refer to when a physician and patient do not share the same language or culture. This can create barriers to providing high-quality care at the end-of-life (EoL). This study explores the intersections of language, culture, geography, and care model in EoL care from the perspectives of palliative care physicians. METHODS: In this exploratory-descriptive qualitative study, semi-structured interviews (1-h) were conducted virtually between July and November 2023. We interviewed 16 family physicians with experience providing linguistic and/or culturally discordant palliative/EoL care in various urban, suburban, and rural regions of Ontario, who practiced at community and hospital outpatient clinics, home-based care, or long-term care homes. We used reflexive thematic analysis to identify themes across the interviews guided by the intersectionality theoretical framework. RESULTS: We identified three themes 1) Visible barriers to care access due to the inability to communicate accurate information and insufficient time spent during appointments with patients; 2) Invisible barriers to care access, shaped by the Eurocentric approach to palliative care and physicians' lack of awareness on cultural discordance; 3) Workplace supports that currently exist and interventions that physicians would like to see. Community physicians following fee-for-service models were less likely to have access to professional interpreter services. Physicians in long-term care emphasized resource limitations to providing culturally-appropriate care environments. CONCLUSION: Cultural discordance required awareness of personal biases, while language discordance hindered basic communication. These findings will be useful in informing clinical practice guidelines and mobilizing policy-level change to improve palliative/EoL care for patients from linguistic and cultural minority groups.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".