Engaging local governments in health promotion and chronic disease prevention activities: the case of Local Health Security Funds in Thailand
Bibliographic record
Abstract
Background: Health care systems should use community-driven activities to promote health and prevent disease to address the challenges from noncommunicable diseases (NCDs) such as diabetes mellitus and high blood pressure. In Thailand, Local Health Security Funds (LHSF) are an initiative to encourage local governments to play a more active role in promoting health. Universal Health Coverage provides funding for this initiative. However, the effectiveness of such initiatives has not been fully assessed. Objectives: To investigate the effectiveness of LHSFs in conducting activities to promote health and prevent disease related to diabetes and hypertension. Methods: We administered a questionnaire to local governments responsible for LHSFs in April 2014 to survey information about their communities, leadership, and activities to promote health. Results: Complete answers to our questionnaire were provided by 1,144 respondents (98.4%). About 94% of those surveyed had already joined LHSFs. Most LHSFs implemented a variety of community activities to promote health, and prevent diabetes and hypertension. We classified these activities into 5 main areas according to the Ottawa Charter. LHSFs most commonly strengthened community action, while building a local health policy was least common. Only 20.8% of the LHSFs had implemented activities in all 5 areas. A number of factors were associated with the activities, including the development of networks and personal skills. Conclusions: LHSFs are useful for engaging local governments in promoting health, and preventing diabetes and hypertension in their communities. Good relationships between local government leaders and public health officers are linked to more effective LHSFs. Keywords: Community, health promotion, local governments, Thailand, NCDs
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".