Qollateral: The Impact of QAnon on Loved Ones and the Potential for P/CVE Programs to Help
Bibliographic record
Abstract
Since 2017, the conspiracy theory known as QAnon has boomed in popularity and spread across national borders. While QAnon is linked to various violent criminal acts, including the January 6th riots on Capitol Hill in Washington, D.C., there is abundant anecdotal data to suggest QAnon also has destructive relational effects on the loved ones of its adherents. While these Q-believers and their loved ones would benefit from psychosocial support, they either do not seek help or are unable to find the type of support they need. By conducting an original survey of 473 family members and friends of Q-believers, this study adds to a nascent but growing body of research documenting the negative collateral effects of conspiracies on loved ones and their need for professional and psychosocial support. Our findings indicate that younger, immediate family members who live with the Q-person experience the greatest negative impacts from their loved one’s belief in QAnon. While this group expressed the highest level of need and desire to access psychosocial support services, they also reported the most barriers to accessing these services. Among these barriers, many respondents identified a lack of QAnon-informed or -specialized support services. These findings suggest that programs aimed at preventing and countering violent extremism (P/CVE) are uniquely positioned to help Q-believers and their loved-ones, as well as to build capacity among health and social service providers to increase the support available to this population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".