Preventing Eating-Related and Weight-Related Disorders
Bibliographic record
Abstract
This book presents a collection of writings by expert researchers from Canada, the United States, and Australia who are committed to finding common cause and common ground in the prevention of eating disorders and obesity. The ten chapters in this book seek to create a new public health approach to the prevention of weight-related disorders, one that counters the confusion and frustration from public policies, messages, and programs that recipients of prevention efforts often experience. The first section looks at prevention from a public health perspective, and the second section highlights theories from risk and resilience research that can inform the prevention of weight-related disorders. The contributions are varied in their theories and models, but woven throughout is the theme of collaboration in changing public institutions and social systems that promotes universal prevention and fosters mental health and resilience. Unique methods of linking systems and fostering partnerships across sectors and disciplines are highlighted, and readers are exposed to innovative ideas of how to move the field of prevention science forward to reduce the onset of negative body image, unhealthy weight management, eating disorders, and disordered eating. Preventing Eating-Related and Weight-Related Disorders is the second in a series of titles from The Community Health Systems Resource Group at The Hospital for Sick Children. This series will educate researchers, policy-makers, students, practitioners, and interested stakeholders on such topics as early intervention in psychosis, aggressive behaviour problems, eating-related disorders, and marginalized youth in educational contexts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".