Neurocognitive impairment in a large sample of homeless adults with mental illness
Bibliographic record
Abstract
OBJECTIVE: This study examines neurocognitive functioning in a large, well-characterized sample of homeless adults with mental illness and assesses demographic and clinical factors associated with neurocognitive performance. METHOD: A total of 1500 homeless adults with mental illness enrolled in the At Home Chez Soi study completed neuropsychological measures assessing speed of information processing, memory, and executive functioning. Sociodemographic and clinical data were also collected. Linear regression analyses were conducted to examine factors associated with neurocognitive performance. RESULTS: Approximately half of our sample met criteria for psychosis, major depressive disorder, and alcohol or substance use disorder, and nearly half had experienced severe traumatic brain injury. Overall, 72% of participants demonstrated cognitive impairment, including deficits in processing speed (48%), verbal learning (71%) and recall (67%), and executive functioning (38%). The overall statistical model explained 19.8% of the variance in the neurocognitive summary score, with reduced neurocognitive performance associated with older age, lower education, first language other than English or French, Black or Other ethnicity, and the presence of psychosis. CONCLUSION: Homeless adults with mental illness experience impairment in multiple neuropsychological domains. Much of the variance in our sample's cognitive performance remains unexplained, highlighting the need for further research in the mechanisms underlying cognitive impairment in this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".