Bridging gaps at key intersections: Strategies to improve early intervention and treatment access for eating disorders
Bibliographic record
Abstract
ABSTRACT Mental health is at the forefront of discussions in healthcare, education, and social settings, yet eating disorders remain poorly understood and inadequately treated. This paper presents evidence on risk factors for insufficient recognition and intervention for eating disorders across clinical and community healthcare settings and proposes actionable strategies to improve awareness and early intervention for eating disorders. Specifically, gaps in eating disorder awareness and treatment access are exacerbated at two key intersections within health and social systems. First, eating disorders manifest themselves at the intersection of mental and physical categories of health, which places them at risk of being misunderstood, poorly diagnosed, and insufficiently intervened upon. Second, the peak onset of eating disorders falls at the intersection of adolescence and young adulthood, which is a period of rapid developmental, social change, and transitions in care. This analysis highlights how systemic issues within existing social and health systems underlie these intersections and contribute to the continued stigmatization and inadequate treatment access for eating disorders. Given their increased incidence and severity, there is an urgent need to address both the individual and societal burden of these disorders. Healthcare systems must prioritize coordination between physical and mental health practices and improve transitions in care from pediatric and adult healthcare services. Identifying gaps at intersections provides the opportunity to make concrete progress toward improving awareness and treatment.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".