Translation and cross-cultural adaptation of the Vietnamese version of the Diabetes Distress Scale
Bibliographic record
Abstract
The Diabetes Distress Scale (DDS) is a valid instrument to measure diabetes distress included in American Diabetes Association and Canadian Diabetes Association guidelines but not available in Vietnamese. This study translated and culturally adapted the DDS to assess diabetes distress of Vietnamese type 2 diabetics and evaluated its internal consistency, face and content validity. The translation process followed standard guidelines for adaptation of an instrument: forward translation, back translation, synthesis, evaluation by an expert panel and pretest. The expert panel included three English specialists as linguistic experts and six content experts in multidisciplinary areas relevant to the study. The pretest was conducted on a sample of 31 type 2 diabetics in the Endocrinology outpatient clinic at Trung Vuong hospital. Content validity was determined based on experts’ concurrence using content validity index for items (I-CVI). Face validity is assessed by participants in pretest. Internal consistency was measured using Cronbach’s alpha. Final version was equivalent with the original English version and easy to understand. I-CVI of 17 items were 1.00 in linguistic experts and greater than 0.83 in content experts. All 31 participants involved in the pretest commented that the items were very clear and acceptable regarding their socioeconomic background. Cronbach’s alpha coefficient was 0.76 – 0.93 for each subscale and 0.94 for the overall. Vietnamese version of the DDS was reliable, face and content-valid to assess diabetes distress in type 2 diabetics among Vietnamese.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".