Early Intervention Programs for Adolescents and Young Adults With Eating Disorders
Bibliographic record
Abstract
What Is the Problem? How Might Early Intervention Help Fix the Problem? The number of adolescents and young adults living with eating disorders is on the rise. This increase was especially noticeable during the height of the COVID-19 pandemic, with more than a 50% increase in the number of young women being hospitalized with an eating disorder. Early intervention programs are those delivered by community or health care–based organizations that offer interventions to treat adolescents and young adults living with eating disorders within the first 3 years of diagnosable disorder, with the intention of providing earlier access and preventing disease progression. What Did We Do? Advisors with lived experience of eating disorders shared their perspectives and priorities to help reviewers contextualize the evidence and interpret the findings in the literature. Advisors highlighted their treatment experiences and priorities for early intervention, highlighting equity considerations and challenges. We conducted a literature search to identify, gather, synthesize, and summarize relevant evidence to inform our understanding of the clinical effectiveness and clinical harms of early intervention programs. A search of the economic literature was conducted to identify economic evaluations of early intervention programs to treat adolescents and young adults living with eating disorders. Based on an assessment of the clinical evidence, the uncertainty and heterogeneity of the information precluded a de novo cost-effectiveness analysis (CEA). As such, a narrative summary of the health care resources required to implement an early intervention program for adolescents and young adults living with eating disorders was conducted. What Did We Find? Advisors with lived experience of eating disorders described a need for greater access to specialized services focused on eating disorder treatment, equity, capacity building, and culture change. Specific treatment approaches mentioned included family-based treatment, cognitive behavioural therapy, peer support, and group therapy. We identified 14 studies related to the clinical effectiveness of early intervention programs. We did not identify any studies evaluating clinical harms. The findings from included studies suggest that earlier engagement and access to eating disorder support could have clinical benefits; however, interpretation of these findings are uncertain due to various factors. No evidence was identified in the search for information on the cost-effectiveness of early intervention programs for the treatment of adolescents and young adults living with an eating disorder. The resources needed to run early intervention programs (or other similar interventional programs) to treat eating disorders may include administration, staffing, training, IT support and infrastructure, and other overhead costs related to the location in which the service is provided. What Does This Mean? The clinical evidence suggests that investment of health care resources into early intervention programs shows potential for overall benefit and may help address challenges with access to treatment, which was identified as an issue by those with lived experience. The human and financial resources required to implement early intervention programs will vary depending on the treatment options and treatment frequency chosen. The demands on an already limited pool of specialized health care resources in eating disorder care are important considerations when choosing whether to implement any new eating disorder treatment programs. Training and recruiting of specialized health care providers will be a key implementation consideration for any new early intervention program for the treatment of eating disorders. Further consultation with a diverse group of adolescents and young adults with lived experience with eating disorders might be beneficial to inform implementation of early intervention programs within the Canadian context.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".