Obesity, body weight stigma and biomedical oversimplification: moving beyond BMI
Bibliographic record
Abstract
Abstract Body Mass Index (BMI) is one of the most important factors considered to analyse health status. High BMI scores are usually associated with physical or mental disorders, such as cardiovascular disease, diabetes, inflammation, dyslipidemia and depression. However, obesity is usually characterised by the presence of various comorbidities that should be addressed as part of a unique, more complicated picture. Emerging evidence suggests that a deeper acknowledgment of the complexity of obesity as disease, including its comorbidities, should take place in the near future. Overweight, obesity and related disorders have been associated with direct healthcare-related costs and the indirect costs of productivity loss, mostly due to the presence of comorbidities rather than to body weight in general. The belief that weight loss is the best recommendation for people with high BMI as the only path to health could be questioned. In fact, it would seem that weight loss in some subjects does not necessarily coincide with any health improvement. Thus, the relationship between weight status and health expenditure is complex with particular reference to the fact that costs of disease are higher in high-comorbid profiles independently of weight status. Over the last years, researchers have also studied some conditions, such as metabolically healthy overweight and obesity. Recent studies deal with alternatives to conventional weight-loss approaches that could be more effective in health terms. Conventional methods of weight loss are based on calorie restriction and increased energy expenditure instead of unconventional methods, such as the weight-neutral program, which is based on the crucial concept of “mindfulness” to underline the importance of intuitive eating, self-care, pleasurable exercise and size-acceptance. The health benefits related to such types of approaches include physical, psychological and behavioural improvements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| 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".