Conspiracy Theories, Psychological Distress, and Sympathy for Violent Radicalization in Young Adults during the COVID-19 Pandemic: A Cross-Sectional Study
Bibliographic record
Abstract
The COVID-19 pandemic has spread uncertainty, promoted psychological distress, and fueled interpersonal conflict. The concomitant upsurge in endorsement of COVID-19 conspiracy theories is worrisome because they are associated with both non-adherence to public health guidelines and intention to commit violence. This study investigates associations between endorsement of COVID-19 conspiracy theories, support for violent radicalization (VR) and psychological distress among young adults in Canada. We hypothesized that (a) endorsement of COVID-19 conspiracy theories is positively associated with support for VR, and (b) psychological distress modifies the relationship between COVID-19 conspiracy theories and support for VR. A total of 6003 participants aged 18-35 years old residing in four major Canadian cities completed an online survey between 16 October 2020 and 17 November 2020, that included questions about endorsement of COVID-19 conspiracy theories, support for VR, psychological distress, and socio-economic status. Endorsement of conspiracy theories was associated with support for VR in multivariate regression (β = 0.88, 95% confidence interval (CI) 0.80-0.96). There is a significant interaction effect between endorsement of COVID-19 conspiracy theories and psychological distress (β = 0.49, 95% CI 0.40-0.57). The magnitude of the association was stronger in individuals reporting high psychological distress (β = 1.36, 95% CI 1.26-1.46) compared to those reporting low psychological distress (β = 0.47, 95% CI 0.35-0.59). The association between endorsement of COVID-19 conspiracy theories and VR represents a public health challenge requiring immediate attention. The interaction with psychological distress suggests that policy efforts should combine communication and psychological strategies to mitigate the legitimation of violence.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".