Adverse Childhood Experiences Predict Treatment Drop Out in Adolescents and Young Adults With Eating Disorders
Bibliographic record
Abstract
OBJECTIVE: Childhood adversity is associated with elevated risk of developing an eating disorder. The objective of the current study was to examine whether different types of childhood adversity, such as parental separation or abuse exposure, predicted treatment completion in a sample of adolescents and young adults with eating disorders. METHOD: A retrospective chart review was conducted at an eating disorders clinic in Calgary, Canada. Childhood adversity was measured using the adverse childhood experiences (ACEs) scale. Eating disorders diagnoses were determined by physicians following a comprehensive assessment. Logistic regressions were performed with ACEs as predictors of likelihood to complete treatment and including diagnoses and age as covariates. RESULTS: Data were analyzed for 128 patients aged 11-24. Higher ACE scores were associated with a reduced likelihood of completing treatment before and after adjusting for diagnosis and age (adjusted odds ratio = 0.76). Examining individual ACE items, people were less likely to complete treatment if they were exposed to verbal abuse, physical abuse, emotional neglect, or if they witnessed intimate partner violence or substance abuse in the home. However, after adjusting for diagnosis, age, and other ACE exposures, no individual ACE was an independent predictor of treatment completion. DISCUSSION: This study suggests that ACE exposure increases youth attrition from eating disorders treatment in a stepwise manner. Treatment programs for youth with eating disorders should focus additional effort on early intervention for youth who have dealt with childhood adversities, potentially by implementing trauma-informed practice into assessments 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".