The use of technology in the treatment of youth with eating disorders: A scoping review
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Bibliographic record
Abstract
BACKGROUND: Adolescence and young adulthood is a high-risk period for the development of eating disorders. In recent years, there has been an increase in use of technology-based interventions (TBIs) for the treatment of eating disorders. The objective of this study was to determine the types of technology used for eating disorder treatment in youth and their effectiveness. METHODS: A scoping review was conducted according to PRISMA-ScR guidelines. Four databases were searched. Eligible articles included: (1) a TBI (2) participants with a mean age between 10- and 25-years and meeting DSM-IV or DSM-5 criteria for any eating disorder and (3) qualitative or quantitative designs. Quantitative and qualitative studies were assessed for quality. RESULTS: The search identified 1621 articles. After screening of titles and abstracts, 130 articles were read in full and assessed for eligibility by two raters. Forty-nine (29 quantitative and 20 qualitative, observational, or mixed methods studies) met inclusion criteria. Quality ratings indicated that 78% of quantitative studies had a low risk of bias and 22% had a moderate risk. Technologies reviewed in our study included videoconference therapy, mobile applications, and online self-help. We considered interventions used both within sessions with clinicians as well as those used in between sessions by patients alone. Fifteen of 18 (83%) quantitative studies found that TBIs reduce eating disorder symptomatology, with nine of those reporting medium-to-large effect sizes. Qualitative data was of high quality and suggested that virtual interventions are acceptable in this population. CONCLUSIONS: Although identified studies are of high quality, they are limited in number. More research is needed, particularly regarding videoconferencing and mobile applications. Nonetheless, TBIs show promise for the treatment of eating disorders in youth. TRIAL REGISTRATION: Not applicable.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it