Psychological Risk Factors of Future Drug Offending among Young Offenders in Hong Kong - A Longitudinal Study
Bibliographic record
Abstract
In recent years there is a growing concern on drug offenders in Hong Kong. Despite its over-representation in prison and recidivists, drug offending has seldom been studied systematically in risk factor research. The issue as to whether drug offending has specific psychological risk factors or they share a common set of risk factors with general offending remains largely unaddressed. This research applied a longitudinal design to investigate this issue. Using a data-set on young offenders’ psychological characteristics established in 2004 in the Hong Kong Correctional Services, and re-conviction data retrieved 11 years later in 2015, ANCOVA with planned orthogonal contrasts and Discriminant Function Analysis, Correlation and Regression analyses were used to analyze factors predicting post-release outcomes including recidivism, drug offending, and crime severity. Results revealed two sets of psychological risk factors with little overlap that could predict general recidivism (of all types of crime) and future drug offending. Recidivism could be predicted by low Future Time Perspective and Empathy, and high Assertiveness. Low Empathy was predictive of post-release crime severity of non-drug offending recidivists. Drug offending, in contrast, could be predicted by high Impulsiveness and Social Problem-solving deficits during adolescence. These two variables, together with low Assertiveness, also predicted post-release crime severity of drug-offending recidivists. Implications to future intervention and research were discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".