A developing world perspective on homicide and personality disorder
Bibliographic record
Abstract
High rates of psychotic disorder among special populations of homicide offenders, females, youth and the mentally disordered, have received much investigation. Personality disorder, especially antisocial personality disorder, augments the relative risk ratio of violence, especially in combination with substance use disorder. Few studies of these correlates of violence and especially homicide have been reported in low- and medium-income countries (LMIC). Using the structured clinical interview for DSM diagnosis (SCID), personality disorders were identified in a cross sectional study involving 546 homicide offenders in Jimma prison, Ethiopia. Predictors of personality disorder were determined using multivariate analysis of various demographic and clinical variables, for example, age, psychiatric history and substance use. Out of the 316 offenders who completed the SCID, only 16% fulfilled DSM IV criteria for personality disorder. The rationale for killing, self-defence, anger and revenge (52% of offenders), planning involved in offending (50%) and reasonably high level of relationship functioning (57% married) were different from most data from the high-income countries. Diagnostically relevant cultural factors in LMIC, not in play in high-income countries, may explain the differences in personality disorders similar to other mental disorders and the underlying mediators of homicide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".