From stopgap to opportunity: outcomes across age groups in an intentionally designed, remote eating disorder treatment program
Bibliographic record
Abstract
Telehealth-based intermediate level of care programs for eating disorders largely yield comparable outcomes to in-person settings. However, extant research is primarily based on programs that transitioned to virtual formats in response to the COVID-19 pandemic, rather than those intentionally designed for remote delivery. Additional research is needed to evaluate programs specifically created for telehealth environments (i.e. intentionally-remote) and to understand how outcomes vary across age groups. In this retrospective chart review, clinical outcomes at end-of-treatment for 116 patients enrolled in an intentionally-remote eating disorder treatment program were analyzed, including eating disorder symptomatology, quality of life impairment, depressive symptoms, trait anxiety, body mass index, and percentage of ideal body weight. Mixed ANOVAs revealed significant improvements (ps < .001) from admission to discharge across all outcomes with large effects and no interaction by age, suggesting similar effectiveness across groups. By discharge, mean eating disorder symptomatology scores were below the clinical cut-off, quality of life impairment was within the minor to moderate range, depressive symptoms were mild, and weight restoration benchmarks were achieved for all age groups. While these findings support the potential of remote care as an accessible means of offering effective eating disorder treatment, further research is needed to determine generalizability to diverse populations, assess the impact of program features, and examine longitudinal outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".