Systematic Review of Mental Health Problems and Violent Extremism
Bibliographic record
Abstract
This systematic review assesses the impact of mental health problems upon attitudes, intentions and behaviours in the context of radicalisation and terrorism. We identified 25 studies that measured rates of mental health problems across 28 samples. The prevalence rates are heterogenous and range from 0% to 57%. If we pool the results of those samples (n = 19) purely focused upon confirmed diagnoses where sample sizes are known (n = 1705 subjects), the results suggest arate of 14.4% with aconfirmed diagnosis. Where studies relied upon wholly, or in some form, upon privileged access to police or judicial data, diagnoses occurred 16.96% of the time (n = 283 subjects). Where studies were purely focused upon open sources (n = 1089 subjects), diagnoses were present 9.82% of the time. We then explore (a) the types and rates of mental health disorders identified (b) comparison/control group studies (c) studies that explore causal roles of mental health problems and (d) other complex needs.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".