Review: interactive, multisession, and targeted programmes most effective in preventing eating disorders
Bibliographic record
Abstract
Stice E, Shaw H. Eating disorder prevention programs: a meta-analytic review. Psychol Bull 2004;130:206–27.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q What are the effects of eating disorder prevention programmes, and which features are associated with the most effective outcomes? ### ![Graphic][5] Design: Systematic review with meta-analysis. ### ![Graphic][6] Data sources: PsycINFO, MEDLINE, CINAHL, and Dissertation Abstracts International searched 1980 to April 2003. Hand searches of key journals and bibliographies plus contact with experts for unpublished studies. ### ![Graphic][7] Study selection and analysis: Eligible studies were randomised trials comparing eating disorder prevention programmes (designed to control eating pathology risk factors and current eating pathology) with minimal intervention, assessment only, placebo, or wait list controls. Only studies quantifying change in outcomes between intervention and control groups were included. Effect sizes were calculated for outcomes included in at least 10 trials. Where data were sufficient, and heterogeneity of effect sizes was significant, the following moderators of effect sizes were examined: selective programmes … [1]: {openurl}?query=rft.jtitle%253DPsychological%2Bbulletin%26rft.stitle%253DPsychol%2BBull%26rft.aulast%253DStice%26rft.auinit1%253DE.%26rft.volume%253D130%26rft.issue%253D2%26rft.spage%253D206%26rft.epage%253D227%26rft.atitle%253DEating%2Bdisorder%2Bprevention%2Bprograms%253A%2Ba%2Bmeta-analytic%2Breview.%26rft_id%253Dinfo%253Adoi%252F10.1037%252F0033-2909.130.2.206%26rft_id%253Dinfo%253Apmid%252F14979770%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1037/0033-2909.130.2.206&link_type=DOI [3]: /lookup/external-ref?access_num=14979770&link_type=MED&atom=%2Febmental%2F7%2F3%2F78.atom [4]: /lookup/external-ref?access_num=000189106900002&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".