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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".