Eating disorders and substance use in adolescents: How substance users differ from nonsubstance users in an outpatient eating disorders treatment clinic
Bibliographic record
Abstract
OBJECTIVE: The relationship between eating disorders (EDs) and substance use (SU) has only been briefly described in literature using mainly adult populations. This study examined the prevalence and characteristics of SU among patients of an adolescent ED outpatient treatment program. METHOD: A retrospective chart analysis was conducted to determine and subsequently compare medical status, psychosocial factors, treatment course and outcome between patients with and without SU. RESULTS: Over 60% of patients with SU status (n = 203) reported regularly consuming substances. 33.4% of substance users received a diagnosis involving purging behaviors compared to 5.9% of nonusers. Females composed 96.4% and 81.7% of users and nonusers, respectively. Users reported significantly more self-harm (57.7% of users vs. 38.6% of nonusers) but did not differ significantly in terms of trauma (abuse or victimization; 48.3% of users vs. 44.9% of nonusers). The percentage of ideal body weight significantly improved throughout treatment and did not differ by SU with a mean increase of 5.29% (SD = 13.6) among nonusers compared to 5.45% (SD = 7.5) of users. While users and nonusers did not differ before and after treatment in ED severity, users were more likely than nonusers to drop-out of treatment (41.5% of users vs. 25.2% of nonusers). DISCUSSION: Adolescents with SU benefit from ED outpatient treatment as much as those without SU, however, users are more likely to drop-out. Therefore, treatment should target these adolescents' emotional dysregulation to improve treatment compliance. Further research is necessary to determine the efficacy of such an approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".