Conceptualizing Obesity as a Chronic Disease: An Interview With Dr. Arya Sharma
Bibliographic record
Abstract
Dr. Arya M. Sharma challenges the conventional wisdom of relying simply on "lifestyle" approaches involving exercise, diet, and behavioral interventions for managing obesity, suggesting that people living with obesity should receive comprehensive medical interventions similar to the approach taken for other chronic diseases such as Type 2 diabetes or hypertension. He purports that the stigma-inducing focus on self-failing (e.g., coping through food, laziness, lack of self-regulation) does not address biological processes that make obesity a lifelong problem for which there is no easy solution. Interdisciplinary approaches to obesity are advocated, including that of adapted physical activity. Physical activity has multifaceted impacts beyond increasing caloric expenditure, including improved sleep, better mood, increased energy levels, enhanced self-esteem, reduced stress, and an enhanced sense of well-being. The interview with Dr. Sharma, transcribed from a keynote address delivered at the North American Adapted Physical Activity Symposium on September 22, 2016, in Edmonton, AB, Canada, outlines his rationale for approaching obesity as a chronic disease.
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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.045 |
| Insufficient payload (model declined to judge) | 0.003 | 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".