Predictors of non‐completion of a day treatment program for adults with eating disorders
Bibliographic record
Abstract
Although treatment dropout is common among patients with eating disorders, very few studies have examined predictors of non-completion in day treatment. We investigated various potential predictors of dropout from adult day treatment. Participants were 295 adult patients with a diagnosis of Anorexia Nervosa (restricting or binge-eating/purging subtype), Bulimia Nervosa (BN), Other Specified Feeding or Eating Disorder, or Avoidant Restrictive Food Intake Disorder. Predictors included eating-disorder characteristics, motivation at the commencement of treatment, Body Mass Index (BMI), time spent in treatment and personality dimensions. Logistic regression analyses showed that for patients with a BMI of less than 20 at the start of treatment, low BMI was a significant predictor of staff-initiated termination due to not meeting weight gain goals. Furthermore, completing less than 6 weeks of treatment was associated with staff-initiated termination. For the whole sample, those with higher changes in weight over the course of treatment were less likely to terminate prematurely. None of the other predictor variables yielded significant results. Results of the current study highlight characteristics of patients who are more likely not to complete day treatment and can help identify patients who may be at risk for not succeeding in multi-diagnostic day treatment programs.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".