Specialized interventions for individuals at risk of violent extremism: Autistic clients' experiences and perspectives
Bibliographic record
Abstract
Background Knowledge on the experiences of autistic individuals at risk of violent extremism is emerging, but pathways towards radicalization are still unclear and even less is known about pathways towards desistance and the role of mental health practitioners in the process. Method Through interview and survey data, this study presents the perspectives of seven autistic clients enrolled in a specialized clinic for individuals at risk of violent extremism. Results Results highlight the subjective suffering of autistic participants and show that they use the proposed intervention to improve their wellbeing and their relational network. They feel that their use of the services is associated with a relative disengagement in their radical ideas. Mental health and violent extremism services are generally appreciated and perceived as useful when they are available and adapted but accessing them is difficult and non-adapted interventions may be harmful. Conclusions It is therefore essential to include autistic clients in the therapeutic process and to deliver specialized training to clinicians to increase awareness of adapted tools, develop stronger therapeutic alliance , and create a non-judgemental space for autistic clients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".