Provider- and patient-related determinants of diabetes self-management among recent immigrants: Implications for systemic change.
Bibliographic record
Abstract
OBJECTIVE: To examine provider- and patient-related factors associated with diabetes self-management among recent immigrants. DESIGN: Demographic and experiential data were collected using an international survey instrument and adapted to the Canadian context. The final questionnaire was pretested and translated into 4 languages: Mandarin, Tamil, Bengali, and Urdu. SETTING: Toronto, Ont. PARTICIPANTS: A total of 130 recent immigrants with a self-reported diagnosis of type 2 diabetes mellitus who had resided in Canada for 10 years or less. MAIN OUTCOME MEASURES: Diabetes self-management practices (based on a composite of 5 diabetes self-management practices, and participants achieved a score for each adopted practice); and the quality of the provider-patient interaction (measured with a 5-point Likert-type scale that consisted of questions addressing participants' perceptions of discrimination and equitable care). RESULTS: = .0016). Participants who did not have enough money to manage diabetes were 9% less likely to engage in self-management practices; and participants who rated the quality of their interactions with providers as poor were 16% less likely to engage in self-management practices. CONCLUSION: Financial barriers can undermine effective diabetes self-management among recent immigrants. Ensuring that patients feel comfortable and respected and that they are treated in culturally sensitive ways is also critical to good diabetes self-management.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".