Assessing a Person-Centered and Culturally Sensitive Intervention for Arabic-, Turkish, or Urdu-Speaking Individuals With Type 2 Diabetes: Protocol for a Mixed Methods Realist Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Individuals from ethnic minority backgrounds have a 2.5 times higher incidence of type 2 diabetes (T2D) than ethnic Danes. They often face negative experiences with health care professionals, leading to unequal treatment. A person-centered and culturally adapted treatment approach can improve self-care, diabetes management, and treatment adherence. OBJECTIVE: This mixed methods realist evaluation (RE) protocol aims to understand how a person-centered and culturally sensitive course of treatment for T2D works, for whom it is most effective, and under what circumstances it is likely to be effective. METHODS: ; ≥53 mmol/mol) or unmet individual targets at 2 consecutive visits. The RE follows three phases: (1) developing, (2) testing, and (3) refining initial program theories. Data are collected through semistructured interviews at 4 months (visit 4) and at the end of the intervention (visit 6). Survey data are collected at baseline and at the end of the intervention. We aim to recruit 16 to 20 intervention participants, including at least 2 men and 2 women from each language group. Qualitative data will be analyzed thematically using a predefined codebook, and survey data will be analyzed descriptively. RESULTS: As of December 2024, a total of 13 visits and 4 interviews had been completed. All baseline survey responses have been collected, along with 2 survey responses at the end of the intervention. Data analysis is pending. The results will inform revisions to the initial program theories, refining a comprehensive model that captures interactions between context, mechanisms, and outcomes. We anticipate disseminating the findings in the first half of 2026. CONCLUSIONS: This RE will support the randomized controlled trial by providing insights applicable to real-life clinical settings. The anticipated impact includes aiding the future development and implementation of interventions for the target groups. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69852.
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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 | 0.080 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".