Children of extremist parents: Insights from a specialized clinical team
Bibliographic record
Abstract
BACKGROUND: Data on children who grow up with parents adhering to violent extremism is scant. This makes it extremely delicate to inform policies and clinical services to protect such children from potential physical and psychological harm. OBJECTIVE: This paper explores the predicament of children whose caretakers were referred to a specialized clinical team in Montreal (Canada) because of concerns about risks or actual involvement in violent extremism processes. METHODS: This paper uses a mixed methods concurrent triangulation design. Quantitative data was obtained through a file review (2016-2020). Qualitative data was collected through semi-structured interviews and a focus group with the team practitioners. RESULTS: Clinicians reported the presence of stereotypes in the health and social services network frequently representing religious extremist parents as potentially dangerous or having inappropriate parenting skills while minimizing the perception of risk for parents adhering to political extremism. Children displayed high levels of psychological distress, mainly related to family separation, parental psychopathology, and conflicts of loyalty stemming from familial or social alienation. CONCLUSIONS: Training practitioners to be aware of their own personal and institutional bias may help them to understand the predicament of extremist parents' children and implement systemic, trauma and attachment informed interventions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".