Self-management of Type 2 Diabetes among Mainland Chinese Immigrants in Canada- A Qualitative Study
Bibliographic record
Abstract
The current study used phenomenology to explore how Mainland Chinese immigrants with type 2 diabetes engage in the self-management of diabetes in Canada. A total of 18 participants were interviewed (8 were male and 10 were female). The average age of the participants was 50.7 years old. Overall, participants were highly motivated in their diabetes management. They were seeking information on how to manage their diabetes from both formal and informal channels. The majority of them didn’t include traditional Chinese medication in their treatment due to the negative views they had towards it. They had a fear of western medication because of the possible side effects associated with it; however, many of them had to take medication when their condition was not managed with lifestyle intervention alone. Participants were in favour of lifestyle intervention, including diet management and physical activity. They changed from mindless eating to mindful eating, specifically, reducing their overall food intake with reduced carbohydrate, protein and increased vegetable intake. They acknowledged the importance of physical activity in diabetes management, but encountered more barriers in achieving the desired level of physical activity. Glucose level was used as a biofeedback to the changes they made in western medication, nutrition therapy, and physical activity. Balance and control were achieved as they have learned to manage their condition while living a “normal” life.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".