Intensive community and home-based treatments for eating disorders: a scoping review
Bibliographic record
Abstract
BACKGROUND: Intensive community treatment (ICT) and home-based treatment (HBT) have emerged as valuable alternatives to institution-based intensive treatments (inpatient or day patient) for severe mental illnesses. Although potential benefits of ICT and HBT for eating disorders (EDs) have been proposed, this area of research remains largely unexplored. METHOD: A scoping review was conducted to map the available literature. Four databases (PubMed, PsycInfo, MEDLINE, Web of Science), grey literature, and trial registries were searched. Sources were included if they presented treatments offering more than two planned therapeutic contacts per week for at least part of the program, excluding physical monitoring contacts, for patients diagnosed with any ED across all ages. RESULTS: Forty-six sources met the inclusion criteria (ICT: n = 31; HBT: n = 15), with most studies from Europe (n = 23) and the USA (n = 18). Among these, 28 reported quantitative data, six reported qualitative data, and three employed a mixed-methods approach. The remainder were either protocol papers or service descriptions only. The majority focused on anorexia nervosa (AN) or mixed EDs, with varying study designs and predominantly low to moderate evidence quality. There were no randomized controlled trials. HBTs primarily targeted children and adolescents with AN, emphasizing family-based approaches, while ICTs exhibited greater variability in age groups and diagnoses, frequently combining cognitive behavioral and dialectical behavioral therapies, often alongside family-based components for children and adolescents. Despite high variability in design, quality, and measurements, studies frequently reported improvements in clinical outcomes. Programs were often described as feasible and acceptable, noting patient satisfaction, strong adherence, and cost-effectiveness due to reduced hospital admissions. CONCLUSIONS: Even though there was variability in implementation and methodologies, ICTs and HBTs appear to be promising alternatives to traditional institution-based intensive treatments. Future research requires higher-quality large-scale randomized trials with improved reporting of treatment characteristics and outcomes to enable robust investigations of effectiveness.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".