Het Gebruik Van Risicotaxatie Instrumenten Onder SPV-EN (The Use of Risk Assessment Instruments among Community Psychiatric Nurses)
Bibliographic record
Abstract
Dutch Abstract: Auteur en een groot aantal alumni-collega's van de Universiteit van Maastricht, hebben gekeken welke risicotaxatie-instrumenten SPV-en gebruiken om het risico van recidive in te schatten bij clienten uit de forensische psychiatrie. Met behulp van START kan volgens hen het risico voor anderen, het risico op victimisatie, risico op zelfbeschadigend gedrag, suïcidegevaar, ongeoorloofde afwezigheid, middelenmisbruik en zelfverwaarlozing bij deze forensische groep vastgesteld worden.\nEnglish Abstract: The author and colleagues from the University of Maastricht investigated the use of structured risk assessment instruments in forensic psychiatry. Using instruments such as the START may aid in the assessment of violence, victimization, self-harm, suicide, unauthorized leave, substance use, and self-neglect risk among forensic populations.
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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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".