Personal Beliefs about Experiences in those at Clinical High Risk for Psychosis
Bibliographic record
Abstract
BACKGROUND: Negative beliefs about illness in early psychosis have been shown to have an unfavourable impact on one's quality of life. A shift of focus in psychosis research has been on the detection of individuals considered to be at clinical high risk (CHR) of developing psychosis. Little is known about the impact that beliefs about psychotic like experiences or attenuated psychotic symptoms may have on CHR individuals. AIM: To explore these beliefs in a large sample of young people at CHR of developing psychosis using the Personal Beliefs about Experiences Questionnaire (PBEQ). METHOD: Beliefs about unusual experiences were assessed in 153 CHR individuals with the PBEQ. Prodromal symptoms (measured by the SIPS) and depression (measured by the CDSS) were also assessed. RESULTS: In CHR individuals, holding more negative beliefs was associated with increased severity in depression and negative symptoms. Higher scores on suspiciousness were associated with increased negative beliefs, and higher levels of grandiosity were associated with decreased negative beliefs. Those who later transitioned to psychosis agreed significantly more with statements concerning control over experiences (i.e. "my experiences frighten me", "I find it difficult to cope). CONCLUSIONS: The results suggest that targeting negative beliefs and other illness related appraisals is an important objective for intervention strategies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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, 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".