Enhancing multi-sectoral collaborations for the prevention and control of NCDs in Thailand with a new approach
Bibliographic record
Abstract
BACKGROUND: To achieve the Sustainable Development Goals (SDGs) by 2030, Thailand must engage in effective multi-sectoral collaboration (MSC). However, implementing MSC in Thailand presents significant challenges. Although Thailand had a 2011-2020 MSC strategic plan for the control of non-communicable diseases (NCDs) with the prime minister taking the lead, joined by many non-health ministers, not a single meeting was called over those 10 years. This paper describes the development of a new tool created to enhance MSC between health and non-health sectors in controlling NCDs in Thailand. Stakeholder-engaged research will be used to implement and evaluate this tool. This paper also describes the research planned to test the new approach. METHODS: The authors used two main methods: (1) a narrative review on MSC enhancement and (2) a series of four consultation meetings with key stakeholders - in the health, non-health and academic sectors - to develop a research study to implement and evaluate the new approach. RESULTS: To address previous MSC implementation problems, the proposed novel MSC enhancement approach emphasizes three principles: (1) pursuit of committed-stakeholder involvement at the middle-management level, instead of relying on the top-management level, an approach which has never been successful; (2) production of knowledge to support specific, achievable target policies; and (3) use of a comprehensive set of knowledge-translation activities and knowledge brokers to solve the problem of ineffective routine official communications between members of the MSC. Using participatory consultations during the research proposal development, middle-level officials from three non-health ministries (the Ministries of Agriculture, Finance and Education) agreed to join the MSC to work together to solve specific problems regarding the control of NCDs. A target-advocated policy for each ministry was formulated and agreed upon by both non-health-sector and health-sector stakeholders. CONCLUSIONS: This new approach (middle-management oriented), if implemented, may encourage more commitment from the Ministries' representatives, policy-relevant knowledge generation and effective communications between ministries involved in an MSC. Ideally, it would complement the conventional approach (top-management oriented) in enhancing the MSC for controlling NCDs, and thereby bring hope for achieving the NCD-related SDGs for Thailand and possibly other countries as well.
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 | 0.038 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".