Role of cultural brokering in advancing holistic primary care for diabetes and obesity: a participatory qualitative study
Bibliographic record
Abstract
OBJECTIVES: Diabetes and obesity care for ethnocultural migrant communities is hampered by a lack of understanding of premigration and postmigration stressors and their impact on social and clinical determinants of health within unique cultural contexts. We sought to understand the role of cultural brokering in primary healthcare to enhance chronic disease care for ethnocultural migrant communities. DESIGN AND SETTING: Participatory qualitative descriptive-interpretive study with the Multicultural Health Brokers Cooperative in a Canadian urban centre. Cultural brokers are linguistic and culturally diverse community health workers who bridge cultural distance, support relationships and understanding between providers and patients to improve care outcomes. From 2019 to 2021, we met 16 times to collaborate on research design, analysis and writing. PARTICIPANTS: Purposive sampling of 10 cultural brokers representing eight different major local ethnocultural communities. Data include 10 in-depth interviews and two observation sessions analysed deductively and inductively to collaboratively construct themes. RESULTS: Findings highlight six thematic domains illustrating how cultural brokering enhances holistic primary healthcare. Through family-based relational supports and a trauma-informed care, brokering supports provider-patient interactions. This is achieved through brokers' (1) embeddedness in community relationships with deep knowledge of culture and life realities of ethnocultural immigrant populations; (2) holistic, contextual knowledge; (3) navigation and support of access to care; (4) cultural interpretation to support health assessment and communication; (5) addressing psychosocial needs and social determinants of health and (6) dedication to follow-up and at-home management practices. CONCLUSIONS: Cultural brokers can be key partners in the primary care team to support people living with diabetes and/or obesity from ethnocultural immigrant and refugee communities. They enhance and support provider-patient relationships and communication and respond to the complex psychosocial and economic barriers to improve health. Consideration of how to better enable and expand cultural brokering to support chronic disease management in primary care is warranted.
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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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".