Systematic Review on the Outcomes of Tertiary Prevention Programs in the Field of Violent Radicalization
Bibliographic record
Abstract
In the last decade, growing concerns about radicalized violence have led governments to make important efforts and invest significant sums of money in developing programs to prevent violent extremism (PVE). Despite these efforts, current knowledge regarding best practices in prevention remains disparate, and the effectiveness of practices used at present has not yet been clearly established. This is especially true for tertiary prevention programs, i.e., those that aim to “deradicalize” and/or disengage individuals from extremist groups and reintegrate them into society. To address this knowledge gap, we conducted a systematic review of the literature published up to 2019 to identify “what works” in tertiary PVE programming. Of the 11,836 studies generated from the searches in this review, 17 were eligible, as they included a sufficiently robust empirical evaluation of a tertiary prevention initiative using primary data. Narrative synthesis of the reviewed studies suggested that deradicalization interventions were harder to implement and less effective on average than disengagement/social reintegration interventions. This was echoed in the intervention modules that were most often described as successful: education, vocational training, and socialization components were preferred to religious education modules or online interventions purposed to challenge violent radical ideologies. The delivery of programs was facilitated by following the risk, needs, and responsivity principles of effective correctional intervention, as well as adequate training of practitioners, cooperation between the staff, good therapeutic alliance, complementary psychological counseling, and involving prosocial family members in the intervention. However, these conclusions rely on studies with substantial methodological limitations that hinder one’s confidence in their results. A screening of studies published between 2020 and 2024 was conducted and largely replicated the conclusions reported herein.
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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 | 0.013 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".