La práctica de la evaluación del riesgo de violencia en España
Bibliographic record
Abstract
La valoración del riesgo de violencia es un requisito fundamental en la toma de decisiones profesionales que implican prevenir, intervenir o informar sobre la conducta de las personas. El uso de herramientas estructuradas mejora la precisión de las evaluaciones basadas en el juicio clínico en contextos psiquiátricos, penitenciarios y jurídicos. Este estudio presenta resultados de la primera encuesta sobre el uso de herramientas de evaluación del riesgo de violencia y sobre su utilidad percibida en España. Las escalas de psicopatía (PCL-R y PCL:SV) y el HCR-20 encabezaron la lista de las herramientas más usadas tanto por elección personal como por requisito institucional. Se ofrecen datos novedosos sobre las prácticas profesionales de evaluación del riesgo de violencia que pueden orientar a los profesionales que desempeñan su tarea en contextos sanitarios, correccionales y forenses, donde los instrumentos estructurados son frecuentemente usados para asistirlos en la toma de<br />decisiones.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".