New approaches to the prevention of eating disorders
Bibliographic record
Abstract
Introduction: The development of effective, cost-effective and widely accessible preventive programs is crucial to reducing the burden of disease related to EDs. Programs using cognitive-behavioral and dissonance-based approaches are most effective for selective prevention. Universal and indicated prevention programs should be further investigated. And programs should be extended to a wider range of ages, races, and cultures, and address multiple public health problems such as obesity and eating disorders, weight-related problems with shared risk factors. The Body Project, MABIC and ZARIMA are successful programs in the prevention of problems related to eating and weight (PRAP). Universal interventions in collaboration with programs for the prevention of drug use or risky sexual behaviors should also be developed. A rigorous evaluation of their efficacy, effectiveness, implementation, and dissemination is necessary. It might be optimal to implement the Body Project with peer-led groups to address the barriers associated with clinician-led interventions. The limitations of traditional programs could be overcome with Internet- and mobile-based interventions. Internet-based interventions could maximize the scope and impact of preventive efforts. However, current scientific evidence for the prevention of EDs online is limited. Internet interventions are less effective than face-to-face ones, with small or medium effect sizes.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".