Health care providers’ perspectives on challenges and opportunities of intercultural health care in diabetes and obesity management: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Migrants often face worse health outcomes in countries of transit and destination because of challenges such as financial constraints, employment problems, lack of a network of social support, language and cultural differences, and difficulties accessing health services. As understanding how the migrant context affects patient-provider engagement is critical to the provision of contextually appropriate care, this study aimed at understanding primary health care provider perspectives on challenges and opportunities of the intercultural care process for migrant patients with diabetes and obesity. METHODS: This qualitative study within a multimethod, participatory research project involved primary care providers in clinics and primary care networks in Edmonton, Alberta, between September 2019 and February 2020. We explored health care providers' approaches to diabetes and obesity management, and experiences of and challenges with intercultural care. We conducted a thematic analysis using an interpretive qualitative approach. RESULTS: We conducted 9 interviews and 4 focus groups and identified 3 themes: a shift from traditional weight loss-centred approaches; relationships and navigating cultural distance; and importance of and limitations in identifying and addressing root causes and barriers. Health care providers encounter considerable nonmedical challenges when supporting immigrant patients, such as navigating cultural distance and working with patients' financial constraints. INTERPRETATION: The nonmedical challenges we identified can hinder the process of chronic disease management. Thus, in addition to educational programs and trainings to enhance the cultural competency of health care providers, incorporating avenues for cultural brokering in health care can provide invaluable support in patient-provider engagements to mitigate these challenges.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".