Global barriers to decision makers for prioritizing interventions for obesity
Bibliographic record
Abstract
The treatment of obesity remains underprioritized. New pharmacologic options for the treatment of obesity have shown effectiveness and safety but are not widely reimbursed. Despite the unmet need and the existence of effective prevention and treatment strategies, substantial barriers exist to effectively address obesity as a disease. The purpose of this scoping review was to investigate the barriers for decision makers in prioritizing interventions for obesity and to seek out interconnection between barriers to prevention and treatment. A scoping review was conducted using a systematic search of both scientific databases and Health Technology Assessment (HTA) databases. Studies that addressed barriers to reimbursement or prioritization of obesity treatment and prevention were included. A total of 26 articles and 14 HTAs were included. Four main barriers for decision makers to prioritize new interventions for obesity were identified: perceptions, knowledge, economics, and politics. There was a high degree of interconnectedness among barriers, as well as large overlaps between barriers in relation to bariatric surgery, pharmacologic treatments, and prevention regulation. Multiple barriers exist that impact decision makers in prioritizing interventions for treating obesity. A strong interconnectedness of the barriers was found, indicating a systems approach to improve global prioritization to address the disease. This study suggests that decision makers should carefully consider all main barriers when addressing the obesity epidemic.
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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 | 0.224 | 0.406 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| 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".