Championing the use of people‐first language in childhood overweight and obesity to address weight bias and stigma: A joint statement from the<scp>European‐Childhood‐Obesity‐Group</scp>(<scp>ECOG</scp>), the<scp>European‐Coalition‐for‐People‐Living‐with‐Obesity</scp>(<scp>ECPO</scp>), the<scp>International‐Paediatric‐Association</scp>(<scp>IPA</scp>),<scp>Obesity‐Canada</scp>, the<scp>European‐Association‐for‐the‐Study‐of‐Obesity Childhood‐Obesity‐Task‐Force</scp>(<scp>EASO‐COTF</scp>), Obesity Action Coalition (<scp>OAC</scp>), The Obesity Society (<scp>TOS</scp>) and the<scp>World‐Obesity‐Federation</scp>(<scp>WOF</scp>)
Bibliographic record
Abstract
Championing the use of people-first language in childhood overweight and obesity to address weight bias and stigma: A joint statement from the European-Childhood-Obesity-Group (ECOG), the European-Coalition-for-People-Living-with-Obesity (ECPO), the International-Paediatric-Association (IPA), Obesity-Canada, the European-Association-for-the-Study-of-Obesity Childhood-Obesity-Task-Force (EASO-COTF), Obesity Action Coalition (OAC), The Obesity Society (TOS) and the World-Obesity-Federation (WOF)Leading voices in the field of obesity research and clinical practice have called for the use of person-first and patient-first language in overweight and obesity clinical practice, research, education, and advocacy communications.While this has been a clear and consistent message in the context of adult obesity, [1][2][3][4][5][6][7] we here aim to highlight the importance of people-first language for childhood obesity.Obesity is a chronic, complex, neurometabolic disease whereby an abnormal or excessive accumulation of body fat results in risk to health.People-first terminology appropriately acknowledges individuals first and avoids defining them by their disease, 8 for example, using the term 'people with obesity' instead of 'obese people'.By extension, respectful person-first communication uses this patient-centred over euphemistic terms or emotional labels that suggest victimization or helplessness.9 In contrast, identity-first or disease-first language puts
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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 | 0.076 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.025 | 0.047 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".