Bibliographic record
Abstract
OBJECTIVE: To assess the associations between staff feelings toward patients and the patients' diagnoses, in view of the fact that clinical reports of such associations have not been corroborated by systematic research. METHOD: At 24 psychiatric units, 143 patients were assessed according to their personality organization, and staff feelings toward these patients were followed for 5 years. The feelings were reported on a feeling checklist twice yearly, and outcome was assessed as the effect size at year 5, using ratings on Kernberg's structural model complemented with ratings on Strauss-Carpenter's function scale. RESULTS: The study showed that it was possible, using discriminant analyses, to separate diagnostic groups by the different feelings that they evoked in the staff. Patients with borderline personality organization (BPO) evoked fewer relaxed and more aggressive feelings, in contrast to patients with psychotic personality organization (PPO). In contrast to patients with neurotic personality organization (NPO), who evoked feelings of sympathy and helpfulness, PPO patients evoked more feelings of insufficiency and disappointment. A stepwise discriminant analysis of reactions to patients with positive treatment outcome separated the 3 personality organizations with 2 functions using only 2 feelings, "relaxed" and "objective." The feeling relaxed separated the NPO patients from the BPO patients, and the feeling objective separated the PPO patients from the other groups. The patients' diagnoses accounted for larger proportions of variance in feelings for the patients with positive outcome. CONCLUSION: The results implied that the patients' different personality organizations evoked different staff feelings in this treatment context and that positive treatment outcome was associated with more pronounced and clear-cut staff reactions.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".