Longitudinal characterization of psychosis among adults living in marginal housing
Bibliographic record
Abstract
People living in marginal or inadequate housing experience increased risk for premature mortality and face accumulating health challenges associated with poverty, substance use, and physical and mental illness. In particular, psychotic disorders, such as schizophrenia or schizoaffective disorder, may be more common. Psychosis, or grossly impaired reality testing, is a key feature of these disorders, but remains poorly understood, due to the heterogeneous course, multifaceted etiology, and complex clinical presentation. As part of a five-year longitudinal study of adults living in urban marginalized housing in Vancouver, Canada, we sought to characterize the consequences, risk factors, and dynamics of psychosis over time. First, we demonstrated that psychotic disorders were a significant risk factor for premature mortality over the study period, beyond other potentially treatable illnesses. Second, through direct clinical interviews each month, we observed a high prevalence of psychosis and psychosis risk factors. Among those without schizophrenia or schizoaffective disorder, the number of days of methamphetamine, powder cocaine, cannabis, or alcohol use predicted dose-related increases in odds of psychosis, without evidence of interaction or reverse causation. Recent trauma, and histories of early-life trauma or brain injury, also had independent effects on psychosis. No relationships with risk factors were demonstrated in the schizophrenia/schizoaffective group. Lastly, we examined how psychosis may evolve over time through the interplay between psychotic symptoms themselves. By assessing symptoms monthly and applying a multilevel dynamic network analytic approach, we disentangled the within-individual temporal dynamics of psychotic symptoms from the stable between-individual differences. Psychotic symptoms fluctuated and were positively reinforcing over time. Delusions had a central role in the symptom network, at both the between-individual and within-individual levels. Delusions were associated with more severe unusual thought content or suspiciousness, but not conceptual disorganization. In the dynamic symptom network, suspiciousness was upstream and hallucinations were downstream in the symptom activation cascade. Dynamic network connectivity was greatest in the group with schizophrenia or schizoaffective disorder. Overall, these studies identify multiple risk factors and psychopathological processes that contribute to the longitudinal characteristics of psychosis and suggest potential targets for intervention and prevention strategies among adults at risk for psychosis.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".