Which psychotic experiences are associated with a need for clinical care?
Bibliographic record
Abstract
BACKGROUND: The aims of this study were to identify (1) the factor structure of anomalous experiences across the psychosis continuum; (2) qualitative and quantitative differences in psychotic experiences (PEs) between "non need-for-care" and two clinical groups: psychosis patients and individuals at ultra high risk (UHR) of psychosis. We aimed to distinguish which types of experiences would be related to malign (need-for-care and/or help-seeking) versus benign outcomes. METHODS: Component scores obtained from a Principal Components Analysis of PEs from lifetime scores on the Appraisals of Anomalous Experience Inventory (Brett et al., 2007) were compared across 96 participants: patients diagnosed with a psychotic disorder (n=37), help-seeking UHR people (n=21), and non-clinical individuals presenting with enduring PEs (n=38). RESULTS: A five-component structure provided the best solution, comprising dissociative-type experiences, subjective cognitive deficits, and three separate components relating to "positive" symptoms. All groups reported "positive" experiences, such as ideas of reference and hallucinations, with the non-clinical group displaying more PEs in the Paranormal/Hallucinatory component than both clinical groups. "Cognitive/Attentional anomalies" was the only component where the clinical groups reported significantly more anomalies than the non-clinical group. However psychosis patients reported more frequent first-rank type symptoms and "hypomanic" type PEs than the other groups. DISCUSSION: "Positive" PEs were common across the psychosis spectrum, although first-rank type symptoms were particularly marked in participants diagnosed with a psychotic disorder. Help-seeking and need-for-care were associated with the presence of subjective cognitive disturbances. These findings suggest that anomalies of cognition and attention may be more relevant to poorer outcomes than the presence of anomalous experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".