Hallucinations in the general population across the adult lifespan: prevalence and psychopathologic significance
Bibliographic record
Abstract
BACKGROUND: Community studies have found a relatively high prevalence of hallucinations, which are associated with a range of (psychotic and non-psychotic) mental disorders, as well as with suicidal ideation and behaviour. The literature on hallucinations in the general population has largely focused on adolescents and young adults. AIMS: We aimed to explore the prevalence and psychopathologic significance of hallucinations across the adult lifespan. METHOD: Using the 1993, 2000, 2007 and 2014 cross-sectional Adult Psychiatric Morbidity Survey series (N = 33 637), we calculated the prevalence of past-year hallucinations in the general population ages 16 to ≥90 years. We used logistic regression to examine the relationship between hallucinations and a range of mental disorders, suicidal ideation and suicide attempts. RESULTS: The prevalence of past-year hallucinations varied across the adult lifespan, from a high of 7% in individuals aged 16-19 years, to a low of 3% in individuals aged ≥70 years. In all age groups, hallucinations were associated with increased risk for mental disorders, suicidal ideation and suicide attempts, but there was also evidence of significant age-related variation. In particular, hallucinations in older adults were less likely to be associated with a cooccurring mental disorder, suicidal ideation or suicide attempt compared with early adulthood and middle age. CONCLUSIONS: Our findings highlight important life-course developmental features of hallucinations from early adulthood to old age.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".