Obesity, laypeople’s beliefs and implications for clinicians and leaders of healthcare organisations
Bibliographic record
Abstract
BACKGROUND/AIM: Overweight and obesity (OAO) is a major and growing public health crisis in the world. There is convincing medical evidence that caloric overconsumption, rather than lack of exercise, is the primary driver of OAO. METHODS: In this translation piece, we summarise our programme of research on laypeople's beliefs about the primary cause of OAO, the origins of these beliefs and implications for clinicians and leadership in healthcare organisations. RESULTS: In contrast to the medical consensus, our research conducted in several countries has found that approximately half of the population mistakenly believes that lack of exercise is the primary cause of obesity. These misbeliefs have consequences: people who mistakenly believe that exercise is the most important factor are more likely to be overweight or obese than people who correctly believe that diet is the primary cause of obesity. We argue that these misbeliefs are caused in part by systematic and multipronged communications efforts by the food and beverage industry-a phenomenon we term 'leanwashing'. CONCLUSIONS: Not only does leanwashing require public policy intervention by the government, healthcare professionals also need to respond appropriately. In this article, we focus on the implications of leanwashing for leaders of public health organisations, health delivery organisations and clinicians.
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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 | 0.028 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".