Counter-radicalisation case management interventions: Findings from a Campbell systematic review
Bibliographic record
Abstract
This article sets out the findings of a Campbell systematic review examining the effectiveness and implementation of case management tools and approaches used to counter radicalisation to violence. This review found that the effectiveness of these tools and approaches remains poorly understood owing to the continued absence of robust impact evaluations. However, by breaking the case management process down into its constituent parts, from client identification to client assessment; case planning, implementation and delivery; monitoring and evaluation; through to exit and transition, this review was able to uncover a robust body of evidence relating to the implementation of different stages of the case management process, and programs as-a-whole. Based on an analysis of 47 studies, the systematic review identified a range of factors that support the effective implementation of case management interventions. These include the availability of relevant tools; strong multi-agency working arrangements; intervention teams holding relevant knowledge and expertise; and adequate resourcing. In contrast, the absence of these factors can inhibit implementation, as can reliance on overly risk-oriented logics; public and political pressure; and the features of the legislative context within which programs are delivered. The paper concludes by outlining the implications of the review, and discusses avenues for future research. • This review examines both whether and how case management tools and approaches work to counter radicalisation to violence. • The effectiveness of these tools and approaches remains poorly understood. • Through an innovative application of a case management framework, the review identifies factors that facilitate and inhibit the implementation of tools and approaches. • Interventions should be underpinned by well-conceived and resourced systems that connect different stages of the case management process. • Less tangible factors, including trust and expertise, and relational processes within intervention and multi-agency teams, are important.
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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 | 0.121 | 0.419 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.041 | 0.043 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".