The Robustness of the Early and Late Start Typology of Criminal Behaviour in Major Mental Disorder: A Conceptual Replication
Bibliographic record
Abstract
The purpose of this study was to examine whether the early and late start typology of criminal behaviour in major mental disorder can be generalized to different populations than evaluated to date and is replicable in two different countries. A Dutch forensic sample (Sample 1) consisted of 260 reports to the court with early start offenders (n = 62) and late start offenders (n = 198). A Canadian civil psychiatric sample (Sample 2) consisted of file information collected from 78 involuntarily hospitalized civil psychiatric patients with an early start group (n = 38) and a late start group (n = 40). In both samples, early and late starters were compared on different domains. Results showed that in general, early starters have a higher risk of having problems in different domains. There were also differences in the early and late start typology between the Dutch and Canadian samples. Our results partially support the early and late starter typology within two different samples. This study showed that early starters typically have a higher risk of problems in different domains. This highlights the importance of different risk management and treatment strategies for both the early start and the late start group.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".