Personality Assessment Inventory profiles of university students with eating disorders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Eating disorders are complex disorders that involve medical and psychological symptoms. Understanding the psychological factors associated with different eating disorders is important for assessment, diagnosis, and treatment. METHODS: This study sought to determine on which of the 22 Personality Assessment Inventory (PAI) scales patients with anorexia nervosa, bulimia nervosa, and eating disorder not otherwise specified (EDNOS) differed, and whether the PAI can be used to classify eating disorder subtypes. Because we were interested in both whether the PAI could be used to differentiate eating disorder subtypes from each other, as well as from other disorders, we also included a group of patients with major depression. RESULTS: The three eating disorder groups did differ significantly from each other, and from the patients with depression, on a number of the PAI scales. Only two PAI scales (Anxiety and Depression), however, exceeded a T-score of 70 for the patients with anorexia nervosa, no scales exceeded a T-score of 70 for the patients with bulimia nervosa or EDNOS, and only two exceeded a T-score of 70 for the patients with depression (Depression and Suicide). A discriminant function analysis revealed an overall correct classification between the groups of 81.6%. CONCLUSIONS: The PAI helps to understand the psychological factors associated with eating disorders and can be used to assist with assessment. Continued investigation using the PAI in an eating disordered population is supported.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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