Delusion or Conspiracy? How Forensic Mental Health Professionals Differentiate Delusional Beliefs From Extreme Radicalized Beliefs
Bibliographic record
Abstract
A growing body of research is beginning to highlight the difficulty clinicians have in distinguishing delusional beliefs from conspiratorial beliefs. This mixed-methods study examined how 198 forensic mental health professionals in Canada and the United States differentiate delusional beliefs from conspiratorial beliefs. Participants were presented with an experimental vignette describing a forensic patient’s symptoms and were asked to diagnose the individual and, if qualified, opine on the defendant’s competency to stand trial. Results showed that idiosyncratic and highly rigid and distressing beliefs significantly predicted the diagnosis of a psychotic disorder, whereas shared beliefs held with low/moderate rigidity and distress significantly predicted the identification of conspiratorial beliefs. Despite participants’ abilities to differentiate delusional and conspiratorial beliefs, some participants reported that they lacked sufficient training in this area. Future research should examine if factors other than the social context and rigidity of the belief influence the differentiation of delusional and conspiratorial beliefs.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".