rehabilitation treatment of patients with psychosis of the residential structure of the mental health operative unit of Torre Annunziata
Bibliographic record
Abstract
Recognizing emotions in oneself and in others is a delicate subject to confront with psychiatric tools, as it refers toa ability that is already compromised and that causes many daily struggles. The proposed year long work consistsof a weekly group project with Intermediate Rehabilitation Structure of ASL Napoli 3 Sud-District 56 participantsand evaluated through the TAS-20 (Toronto Alexitimia Scale) and the ESCQ-45 (Emotional Skills & CompetenceQuestionnaire). The arguments of the study range from the emotional recognition based on mimicking described facialemotions and the sharing of fears and desires, to the recognition of the body parts involved in emotions and meetingsdedicated to managing anger and awareness of one’s reactions. The involved group consists of 10 participants with achronic schizophrenia diagnosis and variable comorbidity with anxiety disorders; the sample is homogeneous by agerange and pharmaceutical dosage.
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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 | 0.001 |
| 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 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".