Identifying Programme‐Led and Focused Interventions for Early Intervention for Eating Disorders in Youth: A Rapid Review
Bibliographic record
Abstract
BACKGROUND: Early intervention (EI) for eating disorders (EDs) has been recognised as important for interrupting the onset of ED symptoms and improving outcomes. Despite growing evidence for EI models of service delivery in community-based settings, there is limited clarity on which specific interventions might best suit young people in these contexts. In this rapid review, we aimed to identify and explore the evidence for programme-led and focused interventions applicable to an EI service delivery model for youth with EDs in community-based settings. METHODS: A systematic search was conducted in MEDLINE, Web of Science, and PsycINFO databases, focusing on interventions that maximise the use of resources, namely guided self-help and group approaches, for individuals under age 30. Studies were screened for eligibility based on intervention brevity (12 sessions or fewer) and relevance to EI models. RESULTS: Findings indicated that while many interventions reduced ED symptoms, few were explicitly designed for EI. Notably, interventions are skewed towards young adult populations, with fewer interventions addressing paediatric needs. Further, the strength of evidence for interventions varied, with many articles reporting on studies with small sample sizes or results illustrating non-superiority to comparison or control. CONCLUSION: This review highlights the need for further research on programme-led and focused interventions tailored to EI, particularly for younger populations, to build an evidence base and improve early-stage ED treatment options in resource-limited community-based settings.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".