New Directions in Treatment Research for Personality Disorders: Effectiveness of Different Levels of Care
Bibliographic record
Abstract
Despite the many promising developments in the PD treatment research literature, several important issues remain inadequately addressed.Among these is the issue concerning the optimal level of care for the psychotherapeutic treatment of PDs.According to Gunderson et al. [14] , the level of care is a multi-dimensional construct that considers containment, intensity, structure, costs per day and duration.Generally speaking, levels of care can be organised hierarchically on the basis of these dimensions (except duration) into inpatient hospitalisation, partial hospitalisation/day treatment and outpatient treatment.The level of care has major ramifications in terms of resource allocation, which ultimately affects service delivery decisions.An additional, perhaps less well appreciated implication is the potential for stigma associated with different levels of care, which can influence patients' willingness to engage with health services.While the relevance of the level of care to the treatment of PDs has received considerable discussion in the literature, research on the matter has been lacking.This is likely owed to formidable methodological challenges, not the least of which is the unlikelihood of being able to randomly assign patients to different levels of care because of practical and ethical constraints.
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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 | 0.032 | 0.098 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.022 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".