Contextualizing Diabetes and Obesity Care for Immigrant and Refugee Populations
Bibliographic record
Abstract
Context: Personalized healthcare strategies are recommended for diabetes and obesity. This approach helps to identify and address root causes and barriers to patients’ health, and provides them with a sense of agency in healthcare. To develop such strategies for migrant patients, there should be a deeper understanding of their situation. Objective: To understand healthcare gaps and opportunities for enhancing diabetes and obesity care for immigrant and refugee populations in primary care. Study Design, Setting and Population Studied: A community-based research, involving 3 qualitative studies conducted in Edmonton, Alberta. Study participants were: 1) members of ethnocultural communities with diabetes and/or obesity; 2) Multicultural Health Brokers (MCHB) - community health workers (CHW) in these communities; and 3) Healthcare providers (HCP). Data set and Analysis: We generated data through interviews and focus groups. Community member (CM) study had 3 focus groups (two groups of 8 males & females (mixed); one of 13 females); and 22 interviews (5 males, 8 females). MCHB study had 10 interviews (females) and 2 observation sessions. HCP study had 4 focus groups (two groups of 6; 2 groups of 8 mixed) and 9 interviews (2 males, 7 females). Data were thematically analyzed. Outcome Measures: CM study explored lived experiences of diabetes and obesity. HCP study examined experiences with caring for patients with diabetes and obesity from ethnocultural communities. MCHB study examined broker roles in relation to primary care. Results: CM study showed that pre- and post-immigration stressors interact synergistically with the lived experience of diabetes and obesity thereby compounding the adverse disease effects. HCP study showed significant challenges navigating cultural distance and trying to address the non-medical issues of migrant patients. MCHB study illustrated their embeddedness in ethnocultural communities playing an invaluable but largely unrecognized role as partners in primary healthcare. Conclusions: Our findings highlight the challenges of intercultural care in primary care settings. CHWs like the MCHB can provide context for healthcare providers, thereby supporting a personalized and context-informed care that understands the intersecting realities of migrant populations. Collaborations with CHWs in primary care have widespread implications for improving healthcare and reducing health disparities for migrants with diabetes and/or obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".