Developing a diabetes prevention education programme for community health-care workers in Thailand: formative findings
Bibliographic record
Abstract
AIMS: The aim of this study was to investigate barriers to and supports for implementing a diabetes prevention education programme for community health-care workers (CHCWs) in Chiang Mai province, Thailand. The study also aimed to get preliminary input into the design of a tailored diabetes prevention education programme for CHCWs. BACKGROUND: Thailand has faced under-nutrition and yet, paradoxically, the prevalence of diseases of over-nutrition, such as obesity and diabetes, has escalated. As access to diabetes prevention programme is limited in Thailand, especially in rural and semi-urban areas, it becomes critical to develop a health information delivery system that is relevant, cost-effective, and sustainable. METHODS: Health-care professionals (n = 12) selected from health centres within one district participated in in-depth interviews. In addition, screened people at risk for diabetes participated in interviews (n = 8) and focus groups (n = 4 groups, 23 participants). Coded transcripts from audio-taped interviews or focus groups were analysed by hand and using NVivo software. Concept mapping illustrated the findings. FINDING: Health-care professionals identified potential barriers to programme success as a motivation for regular participation, and lack of health policy support for programme sustainability. Health-care professionals identified opportunities to integrate health promotion and disease prevention into CHCWs' duties. Health-care professionals recommended small-group workshops, hands-on learning activities, case studies, and video presentations that bring knowledge to practice within their cultural context. CHCWs should receive a credit for continuing study. People at risk for diabetes lacked knowledge of nutrition, diabetes risk factors, and resources to access health information. They desired two-way communication with CHCWs. Formative research supports the need for an effective, sustainable programme to support knowledge translation to CHCWs and at-risk populations in the communities they serve. Ultimately, this should support chronic disease prevention in Thailand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".