Aproximación diagnóstica de psicopatía mediante instrumento autoinformado
Bibliographic record
Abstract
Introducción: Dada la relevancia clínica y social que tiene la psicopatía, resulta útil contar con instrumentos autoinformados para la aproximación diagnóstica a la psicopatía. Objetivos: Evaluar la congruencia interna y la validez convergente del Self Report of Psychopathy-Short Form. Material y métodos: Se evaluaron 208 personas condenadas, alojadas en el Centro de Cumplimiento Penal de la Provincia de Los Andes, Chile. Se utilizaron el SRP-III-SF, el PCL-R y la HCR-20. Resultados: La distribución de valores del SRP-III-SF tuvo una media de 61,6 y fue normal. El Coeficiente de Alfa de Cronbach para el total fue 0,8 y para los factores 1, 2, 3 y 4 fue 0,7, 0, 4, 0,7 y 0,5, respectivamente. La correlación del SRP-III-SF con el PCL-R fue 0,4 (p = 0,01) y con HCR 20, 0,5 (p < 0,001). El riesgo de diagnóstico de psicopatía con el PCL-R aumentó en forma lineal para cada cuartil de la distribución del SRP-SF. El área bajo la curva ROC fue 0,66 (p = 0,05; 95% IC 0,5; 0,8).
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".