When qualitative data contradict quantitative data: diabetes distress in the Chinese‐Canadian community
Bibliographic record
Abstract
AIMS: To use both quantitative and qualitative approaches to characterize the diabetes distress profile of Chinese-Canadians with Type 2 diabetes and to better understand their experience of living with diabetes. METHODS: We recruited 40 Chinese-Canadian adults with Type 2 diabetes who completed a Mandarin- or Cantonese-language diabetes education programme in Richmond, British Columbia. Using a mixed-methods sequential explanatory research design, participants first completed a 15-item Chinese version of the Diabetes Distress Scale, which included three subscales: emotional burden, regimen-related distress, and physician distress. The self-report survey was followed by a semi-structured interview that addressed the following diabetes-related topics: perspectives towards the healthcare team, emotional health, diabetes-related concerns and stressors, diabetes diagnosis experience, and sources of social support and diabetes education. RESULTS: The mean (sd) scores for total distress 1.5 (0.5), emotional burden 1.7 (0.7), regimen-related distress 1.4 (0.5), and physician distress 1.4 (0.9), fell within the 'low distress' range (< 2.0). Qualitative analysis of semi-structured interviews showed that some participants were dissatisfied with diabetes care providers and experienced emotional challenges, particularly early in their diagnosis. Other themes that emerged included eating distress, fear of complications, language barriers, and medication concerns. CONCLUSIONS: Not only did the qualitative findings from semi-structured interviews capture aspects of diabetes distress that were not included in the quantitative survey, it also uncovered inconsistencies between the two datasets. To more accurately understand the diabetes distress experience of any ethnic community, both quantitative and qualitative approaches contribute unique value.
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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 | 0.133 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".