Cultural adaptation and psychometric evaluation of the Kinyarwanda version of the diabetes-39 (D-39) questionnaire
Bibliographic record
Abstract
Abstract Background In recent years, more importance is being given to the assessment of quality of life (QoL) among diabetic patients as a measure of their health and the goal of all health interventions. Other studies have reported a high prevalence of diabetes-related effects on; however, there is a knowledge gap in the region of Sub-Saharan Africa, as is the case for Rwanda, where the prevalence of diabetes is expected to rise over the next decade. The aim of this study is to report on the translation and cultural adaptation of the Diabetes-39 (D-39) questionnaire into the Kinyarwanda and its psychometric properties among diabetic patients in Rwanda. Methods The D-39 questionnaire—a five-scale, disease-specific QoL questionnaire—was translated from English to Kinyarwanda, then back-translated to English. A consensus meeting discussed discrepancies and agreed on changes. Interviews were conducted with 26 participants before producing a final version. For the psychometric evaluation, the adapted version was administered to 309 patients with diabetes mellitus. Participants either came from a separate cluster-randomised controlled trial or were recruited ad hoc for this study. The evaluation included testing internal consistency, known group validity, and construct validity. Results Participants’ mean age was 51 ± 12.7 years with a predominance of women (64%) in the sample. All five scales of the questionnaire showed a good internal consistency, with composite reliability of above 0.7. The five-factor model of the questionnaire was fitted to the 39 items. Although the fit was not exact, there was a satisfactory approximate fit (CFI = 0.93, TLI = 0.92, RMSEA = 0.05). There was a good discriminant validity except for the “social burden” and “anxiety and worry” scales (inter-factor correlation = 0.80). Conclusions Diabetes-39 is a questionnaire developed in English that was adapted and translated into Kinyarwanda. The Kinyarwanda version of D-39 is a reliable and valid instrument to measure QoL among diabetic patients in Rwanda. The questionnaire can be helpful in research and clinical practice improving health outcomes for patients with diabetes in Rwanda and other Kinyarwanda-competent areas in the sub-region. However, certain cross-cultural differences should be considered.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".