Complexity and Stigma of Pediatric Obesity
Bibliographic record
Abstract
Weight stigma is rooted in a fundamental misunderstanding of the origins of obesity, wherein the interplay of behavioral, environmental, genetic, and metabolic factors is deemphasized. Instead, the widespread societal and cultural presence of weight stigma fosters misconceptions of obesity being solely a result of unhealthy personal choices. Weight stigma is pervasive in childhood and adolescence and can affect individuals throughout their life. Although the prevalence of pediatric obesity remains high throughout the world, it becomes increasingly important to understand how weight stigma affects weight and health outcomes in children and adolescents with overweight or obesity, including in those with rare genetic diseases of obesity. We identified and reviewed recent literature (primarily published since 2000) on weight stigma in the pediatric setting. Articles were identified with search terms including pediatric obesity, weight bias, weight stigma, weight-based teasing and bullying, and weight bias in health care. In this narrative review, we discuss the stigma of pediatric obesity as it relates to the complex etiology of obesity as well as describe best practices for avoiding bias and perpetuating stigma in the health care setting.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".