1.I. Pitch presentations: Promoting mental health
Bibliographic record
Abstract
Suicide is a complex phenomenon, and represents the 3rd cause of death in prison.In Italy about 10 suicides per 10,000 inmates and about 150 attempted suicides of 10,000 inmates occur.These data are considerably higher than the general population, and confirm the importance of a project to prevent suicide in prisons . Description of problemIn 2013 the National Bioethics Committee for Health in Prison published a report and established the necessity to create specific protocols to prevent suicide risk.The Regional Council of Tuscany (Italy) affirmed the necessity of a synergic work between health personnel and operators of prison to adopt this kind of measures.In 2014 a new biphasic protocol was created and a multidisciplinary staff was constituted.In the 1st phase the new prisoner should be observed, clinically evaluated and tested with the ''Arboleda-Florez Checklist''.The suicide risk profile, calculated through the checklist, let us adopt an individualized therapeutic plan, ensure an adequate surveillance and the best location for the prisoner.In the 2nd phase, the prisoner should be monitored carefully, signaling promptly any changes in his psychological status. ResultsIn 2015 the nurses were trained to administer the 0 Arboleda Florez 0 check list.The staff met 19 times and 85 new prisoner were clinically evaluated after a week from their entrance.The 25% of new admitted resulted to be at 0 medium risk 0 .The 46% of the newcomers resulted to be 0 adapted 0 (with a low risk).In 2 cases, the psychiatric examination classified the prisoners at 0 high/very high risk 0 so a great surveillance measure was immediately activated. LessonsThe protocol tried to develop new useful strategies for the prevention, monitoring and management of suicide risk in prison .The multidisciplinary management of the inmate patient demonstrated that the collaboration between several professional figures is fundamental for an efficient prevention of this serious problem.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.207 | 0.113 |
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".