Bibliographic record
Abstract
Given that the consequences of eating disorders (EDs) and disordered eating are serious and treatment is often expensive with limited effectiveness, efforts are needed to prevent the onset of EDs and disordered eating. This chapter presents a definition for prevention (including the different levels of prevention and how the level informs the type of intervention), compares and contrasts the individual and population health approaches to prevention, and describes how the population health approach can inform etiologic research on EDs as well as prevention efforts. The chapter highlights some key challenges and proposes next steps for the population health approach to the prevention of EDs. However, while all three forms (primary, secondary, and tertiary) of prevention are needed, the chapter argues that universal population-based approaches will be paramount in reducing the incidence of EDs. It also discusses the determinants of EDs.
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 | 0.047 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.016 | 0.034 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".