Analyzing Linguistic and Culturally Discordant Care from the Perspectives of Nurses and Nurse Practitioners in Ontario, Canada
Bibliographic record
Abstract
Abstract Background and Objectives: Despite Canada's Official Languages Act, linguistic concordant care directives are lacking. This study investigates challenges faced by Ontario's nurses and nurse practitioners in caring for patients from minority linguistic and ethno-cultural groups, along with their adopted strategies. Approach: Adopting a qualitative descriptive approach, this study engaged nurses and nurse practitioners from diverse practice settings across Ontario through semi-structured interviews. Our bilingual recruitment strategy used convenience and snowball sampling from existing professional networks and through social media. Data were analyzed with Reflexive Thematic Analysis within an intersectionality framework. Results: Nurses commonly used professional interpreters and Google Translate. Major challenges included high costs and inconsistent access to interpretation services due to organizational and funding constraints. Nurses needed flexibility to adapt to each patient's needs and commented on the significant time required for care in linguistically and culturally discordant encounters. They emphasized understanding cultural nuances and sensitivity as integral to a holistic care model that encompasses physical, psychological, and social aspects. Nurses often pursued cultural-competency training independently and sought mentorship to improve their care quality for minority populations. Continuous professional development and skill advancement were crucial for managing the complexities of linguistic and cultural discordance in care. Conclusion: The study highlights the need for accessible tools to navigate linguistic and cultural barriers, and for continuous education and mentorship to foster culturally competent care. These elements are crucial for nurses to provide equitable, high-quality care to 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| 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".