P02.309 Psychiatric morbidity among juvenile drug offenders
Bibliographic record
Abstract
present the worker with heavy professional, relational and emoscales: Montgomery-Asberg (MADRS) and Zung.Instrument that tional stress.investigated alexithymia was Toronto Alexithymia Scale (TAS-26).Many variables are involved: the organisational structure, the individual factors, the historical and cultural factors and the policies and strategies for intervention.In the '90's, the philosophy and the policy of Reduction of Harm and the philosophy of Recuperation and Rehabilitation appeared to be the prevalent working guidelines in these Services.Thus it appears significant to know and analyse the different legislation in the two countries, the typologies of interventions and the organisation of the services and evaluate the presence and level of stress in the workers.Based on the sample of outpatient subjects, it was shown that hypertensive patients with affective disorders have the higher level of alexihtymia.At the same time the patients with alexithymia demonstrate discrepancy between the level of arterial blood pressure and their subject sensations.The hypothesis of the research is that the workers' stress is in relation to the objectives and styles of intervention in the Service.We conclude that alexithymia, that is, poor ability to experience and express emotions and sensations is associated with hypertension.The disturbance of treatment by the hypertensive patients with alexithymia and affective disorders is associated with poor ability to experience sensations.So treatment of the patients with hypertension had to correlate with the correction of alexithymia.PO2.308 PO2.306
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".