Using DSM Axis II Information to Predict Outcome in Short-term Individual Psychotherapy
Bibliographic record
Abstract
The present study considered three methods of using DSM Axis II information to examine the effect of personality disorder on outcome in two forms of short-term, individual psychotherapy (interpretive and supportive). The first method involved examining whether the presence of any personality disorder influenced treatment outcome. The second method involved examining the effect of the number of personality disorders on outcome. The third involved examining outcome for specific personality disorders. The study found that a diagnosis of any personality disorder did not influence the outcome of therapy. In contrast, the number of personality disorders was significantly related to outcome at post-therapy and at 12-month follow-up. The findings indicated that a greater number of personality disorders was associated with less favorable outcome across both forms of therapy. This supports the notion that personality pathology is more severe when it involves a greater number of personality disorders. In an exploratory set of analyses, the study also found some evidence of differences in outcome for specific personality disorders.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".