Type 2 Diabetes Mellitus among Immigrants in Canada: A Scoping Review on Self-Management
Bibliographic record
Abstract
BACKGROUND: Diabetes, a chronic disease commonly experienced by immigrants in Canada, can be complicated by cardiovascular and cerebrovascular disease, non-traumatic lower extremity amputation, diabetic retinopathy, and end-stage renal disease. Immigrants from Africa, South Asia, and Latin America are at risk of diabetes because of genetic, sociocultural, environmental, and economic factors. Self-management practices are critical in preventing poor outcomes for individuals with diabetes. OBJECTIVES: This scoping review identifies gaps in the range, scope, nature, and characteristics of self-management practices among immigrants with type 2 diabetes in Canada. METHODS: The review was initiated by accessing 152 primary studies and peer-retrieved articles published in English and retrieved from PubMed, CINAHL, Medline, Scopus, grey literature, and ProQuest Dissertation and Theses databases. After reviewing the abstracts and removing studies that failed to meet inclusion and exclusion criteria, 12 studies were selected for the review. RESULTS: Self-management of type 2 diabetes among Canadian immigrants is influenced by language proficiency, finances, patient-provider preferences, and support from family, health providers, and peers. Length of stay in Canada, acculturation, and cultural beliefs were also found to impinge on diabetes self-management in immigrants. CONCLUSION: More information about the influence of religion, the influence of immigration, and refugee status in specific ethnic groups, as well as studies on the lived experiences of immigrants with type 2 diabetes in Canada, are needed to guide nursing care and improve health outcomes of immigrants with type 2 diabetes mellitus.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".