Characterizing the obesogenic environment: the state of the evidence with directions for future research
Bibliographic record
Abstract
Despite the explosion of obesogenic environment research within the last decade, consensus on what constitutes the very environment we are trying to measure has not yet been reached. This presents a major challenge towards our understanding of environmental research for obesity, and the development of a desperately needed contextualized evidence base to support action and policies for curbing this epidemic. Specifically, we lack the application of a cohesive definition or framework, which creates the potential for confusion regarding the role of the environment, misinterpretation of research findings and missed opportunities with respect to possible avenues for environmentally based interventions. This scoping review identified primary studies and relevant reviews examining factors related to body mass index, diet and/or physical activity with respect to the obesogenic environment. Using a comprehensive framework for conceptualizing the obesogenic environment, the Analysis Grid for Environments Linked to Obesity (ANGELO), we identified 146 primary studies, published between January 1985 and January 2008, that could be characterized according to the dimensions of ANGELO. Gaps in the literature were clearly identified at the level of the macro-environment, and the political and economic micro-environments, highlighting key areas where further research is warranted if we are to more fully understand the role of the obesogenic environment.
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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 | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".