Sistematización de la evaluación de riesgo de violencia con instrumentos de juicio profesional estructurado en Cuenca, Ecuador
Bibliographic record
Abstract
Introduction: The way professionals of mental health carry out violence risk assessment and intervention planning has an impact on judicial decisions, social wellbeing and professional responsibility. Objectives: To determine the reliability of the psychopathy evaluation instruments and structured professional guides in violence risk assessment used at the Institute of Criminology and Family Psychosocial Intervention of the University of Cuenca, Ecuador. Method: Previously trained pairs of psychologists and social workers assessed simultaneously 37 cases, who were transferred to the Institute, using structured violence risk assessment instruments -HCR 20 and SARA- and psychopathy evaluation instrument -Hare PCL-R-. Indicators of internal agreement and consistency were calculated. Results: The agreement of the assessment of the risk of violence towards the couple was excellent. The intraclass coefficient was respectively 0.76 and 0.90 for psychologists and social workers. The indicators for the different sections of the HCR-20 and SARA were also excellent, ranging between 0.75 and 0.94. The indicator for PCL-R total was 0.96. Conclusions: The results support the reliability of these instruments in Ecuador provided the users receive adequate training. The use of these instruments contributes to the systematization and transparency of the risk assessment procedures and the protection of the professional responsibility. [1] SARA: Spousal Assault Risk Assessment [2] Hare PCL-R: Hare Psychopathy Checklist-Revised
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".