Why Do People With Eating Disorders Drop Out From Inpatient Treatment?
Bibliographic record
Abstract
Dropout rates from inpatient treatment for eating disorders are very high and have a negative impact on outcome. The purpose of this study was to identify personality factors predictive of dropout from hospitalization. A total of 64 adult patients with anorexia nervosa consecutively hospitalized in a specialized unit were included; 19 patients dropped out. The dropout group and the completer group were compared for demographic variables, clinical features, personality dimensions, and personality disorders. There was no link between clinical features and dropout, and among demographic variables, only age was associated with dropout. Personality factors, comorbidity with a personality disorder and Self-transcendence dimension, were statistically predictive of premature termination of hospitalization. In a multivariate model, these two factors remain significant. Personality traits (Temperament and Character Inventory personality dimension and comorbid personality disorder) are significantly associated with dropout from inpatient treatment for anorexia nervosa. Implications for clinical practice, to diminish the dropout rate, will be discussed.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".