The role of cultural competence in health care to improve communication between immigrant patients and health-care providers in Ottawa, Canada
Bibliographic record
Abstract
Purpose Cultural competence has been recognized as an essential process in reducing racial and ethnic health-care disparities and improving equity in health care. Recent immigrants to Canada encounter a different and unfamiliar health-care system. This study aims to focus on examining the role of cultural competence in improving communication between immigrant patients and their health-care providers. Design/methodology/approach Using an exploratory approach, four focus group discussions with a sample of recent immigrants were conducted to gain insights from immigrant patients’ perspectives with regard to communicating with their health-care providers. The focus group discussions were analyzed using a thematic analysis approach. Campinha-Bacote’s Cultural Competence Model was used for this study as its theoretical framework. Findings The focus group findings revealed that the lack of cultural competence among health-care providers and language barriers are major issues that impact the health-care experiences of immigrant patients. Health-care organizations may use these findings to better inform their decision-making with regard to effective patient–provider communication. Originality/value This study advances the line of research that examines patient–provider communication by adding diverse immigrant patients’ perspectives. The findings can inform the design of cultural competence strategies for health-care organizations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".