Knowledge exchange in the Pacific: The TROPIC (Translational Research into Obesity Prevention Policies for Communities) project
Bibliographic record
Abstract
BACKGROUND: Policies targeting obesogenic environments and behaviours are critical to counter rising obesity rates and lifestyle-related non-communicable diseases (NCDs). Policies are likely to be most effective and enduring when they are based on the best available evidence. Evidence-informed policy making is especially challenging in countries with limited resources. The Pacific TROPIC (Translational Research for Obesity Prevention in Communities) project aims to implement and evaluate a tailored knowledge-brokering approach to evidence-informed policy making to address obesity in Fiji, a Pacific nation challenged by increasingly high rates of obesity and concomitant NCDs. METHODS: The TROPIC project draws on the concept of 'knowledge exchange' between policy developers (individuals; organisations) and researchers to deliver a knowledge broking programme that maps policy environments, conducts workshops on evidence-informed policy making, supports the development of evidence-informed policy briefs, and embeds evidence-informed policy making into organisational culture. Recruitment of government and nongovernment organisational representatives will be based on potential to: develop policies relevant to obesity, reach broad audiences, and commit to resourcing staff and building a culture that supports evidence-informed policy development. Workshops will increase awareness of both obesity and policy cycles, as well as develop participants' skills in accessing, assessing and applying relevant evidence to policy briefs. The knowledge-broking team will then support participants to: 1) develop evidence-informed policy briefs that are both commensurate with national and organisational plans and also informed by evidence from the Pacific Obesity Prevention in Communities project and elsewhere; and 2) collaborate with participating organisations to embed evidence-informed policy making structures and processes. This knowledge broking initiative will be evaluated via data from semi-structured interviews, a validated self-assessment tool, process diaries and outputs. DISCUSSION: Public health interventions have rarely targeted evidence-informed policy making structures and processes to reduce obesity and NCDs. This study will empirically advance understanding of knowledge broking processes to extend evidence-informed policy making skills and develop a suite of national obesity-related policies that can potentially improve population health outcomes.
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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 | 0.057 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".