Why we struggle to make progress in obesity prevention and how we might overcome policy inertia: Lessons from the complexity and political sciences
Bibliographic record
Abstract
Despite evidence for the effectiveness of policies that target obesogenic environments, their adoption remains deficient. Using methods and concepts from complexity and political science (Stock-and-Flow analysis and Punctuated Equilibrium Theory) and a qualitative literature review, we developed system maps to identify feedback loops that hinder policymaking on mitigating obesogenic environments and feedback loops that could trigger and sustain policy change. We found numerous self-reinforcing feedback loops that buttress the assumption that obesity is an individual problem, strengthening the biomedical and commercial weight-loss sectors' claim to "ownership" over solutions. That is, improvements in therapies for individuals with obesity reinforces policymakers' reluctance to target obesogenic environments. Random events that focus attention on obesity (e.g., celebrities dismissing soda) could disrupt this cycle, when actors from outside the medical and weight-loss sector (e.g., anti-weight stigma activists) successfully reframe obesity as a societal problem, which requires robust and politically relevant engagement with affected communities prior to such events taking place. Sustained prioritization of policies targeting obesogenic environments requires shared problem ownership of affected communities and nonhealth government sectors, by emphasizing cobenefits of policies that target obesogenic environments (e.g., ultraprocessed food taxation for raising revenue) and solutions that are meaningful for affected communities.
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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 | 0.060 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".