Feasibility and Acceptability of the HOME Model to Promote Self-Management Among Ethnic Minority Elderly with Type 2 Diabetes Mellitus in Rural Thailand: A Pilot Study
Bibliographic record
Abstract
Ethnic minority elderly (EME) people are recognized as a vulnerable group who have higher prevalence of type 2 diabetes mellitus (T2DM) than the majority of the population. The aim of this study was to explore the feasibility, acceptability, and effect of the HOME model (Home intervention; Online monitoring; Multidisciplinary approach; and Equity and education) specifically for enhancing self-management activities, glycemic control, and satisfaction of EME with T2DM in rural areas in Thailand.In this quasi-experimental study, a single group used a pre-test and post-test, which were conducted as a pilot study to examine the effect of the HOME model.Overall, 23 dyads of EME with T2DM and their family caregivers completed the 12-week intervention. They reported that the HOME model was helpful and motivating, and they reported satisfaction with the service provided. EME with T2DM showed significant reduction of blood glucose level, and significant improvement in self-management activities, happiness, and satisfaction compared with baseline. Family caregivers had also significant improvements in happiness and reported satisfaction with the HOME model.The primary evidence suggested that the HOME model was acceptable and feasible for EME with T2DM and their families in rural Thailand.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".