Systems thinking and complexity science methods and the policy process in non-communicable disease prevention: a systematic scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Given the complex causal origins of many non-communicable diseases (NCDs), and the complex landscapes in which policies designed to tackle them are made and unfold, the need for systems thinking and complexity science (STCS) in developing effective policy solutions has been emphasised. While numerous methods informed by STCS have been applied to the policy process in NCD prevention, these applications have not been systematically catalogued. The aim of this scoping review is to identify existing applications of methods informed by STCS to the policy process for NCD prevention, documenting which domains of the policy process they have been applied to. METHODS AND ANALYSIS: A systematic scoping review methodology will be used. IDENTIFICATION: We will search Medline, SCOPUS, Embase and Web of Science using search terms combining STCS, NCD prevention and the policy process. All records published in English will be eligible for inclusion, regardless of study design. SELECTION: , excluding studies identified as fatally flawed, and determining the credibility and contribution of included studies. SYNTHESIS: We will identify relevant studies, summarising key data from each study and mapping applications of methods informed by STCS to different parts of the policy process. Review findings will provide a useful reference for policy-makers, outlining which domains of the policy process different methods have been applied to. ETHICS AND DISSEMINATION: Formal ethical approval is not required, as the study does not involve primary data collection. The findings of this study will be disseminated through a peer-reviewed publication, presentations and summaries for key stakeholders.
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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 | 0.162 | 0.154 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.020 |
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".