Surviving and thriving in spite of hate: Burnout and resiliency in clinicians working with patients attracted by violent extremism.
Bibliographic record
Abstract
Violent extremism (VE) is often manifested through hate discourses, which are hurtful for their targets, shatter social cohesion, and provoke feelings of impending threat. In a clinical setting, these discourses may affect clinicians in different ways, eroding their capacity to provide care. This clinical article describes the subjective experiences and the coping strategies of clinicians engaged with individuals attracted by VE. A focus group was held with eight clinicians and complemented with individual interviews and field notes. Clinicians reported four categories of personal consequences. First, results show that the effect of massive exposure to hate discourses is associated with somatic manifestations and with the subjective impression of being dirty. Second, clinicians endorse a wide range of work-related affects, ranging from intense fear, anger, and irritation to sadness and numbing. Third, they perceive that their work has relational consequences on their families and friends. Last, clinicians also describe that their work transforms their vision of the world. In terms of coping strategies, team relations and a community of practice were identified as supportive. With time, the pervasive uncertainty, the relative lack of institutional support, and the work-related emotional burden are associated with disengagement and burnout, in particular in practitioners working full-time with this clientele. Working with clients attracted to or engaged in VE is very demanding for clinicians. To mitigate the emotional burden of being frequently confronted with hate and threats, team relations, decreasing clinical exposure, and avoiding heroic positions help prevent burnout. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".