Centering the patient: How patient lived experience can and should shape obesity thinking
Bibliographic record
Abstract
Obesity is a complex disease that affects millions of people across the world, however a clear disconnect emerges between patient and physician beliefs about obesity when comparing the literature with the lived experience of those affected. The voices of people living with obesity are often unheard despite these being crucial to understanding the complexity and management of obesity. Recent updates in diagnosis and management recommendations of obesity are a step towards change, nonetheless the clinical application of these recommendations across healthcare remains to be determined and notably continues to disregard patient lived experiences. Considering recent publications, clinical experience, and testimonials from people with obesity, here we discuss the stigma faced at the individual, public, and institutional levels for people with obesity, how perspectives of the disease differ between patient and physicians across all levels of stigma in recent literature and identify where further knowledge and clinical application is needed to drive change for the treatment of this ever-evolving disease. The future of obesity management needs to prioritize a holistic, patient-centered approach and the first step to achieve this is to understand the disease through the lens of those living with obesity.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.012 |
| 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".