Cognition and Incarceration: Cognitive Impairment and Its Associated Outcomes in Older Adults in Jail
Bibliographic record
Abstract
OBJECTIVES: To determine prevalence of, and outcomes associated with, a positive screen for cognitive impairment in older adults in jail. DESIGN: Combined data from cross-sectional (n=185 participants) and longitudinal (n=125 participants) studies. SETTING: Urban county jail. PARTICIPANTS: Individuals in jail aged 55 and older (N = 310; mean age 59, range 55-80). Inclusion of individuals aged 55 and older is justified because the criminal justice system defines "geriatric prisoners" as those aged 55 and older. MEASUREMENTS: Baseline and follow-up assessments of health, psychosocial factors, and cognitive status (using the Montreal Cognitive Assessment (MoCA)); 6-month acute care use and repeat arrest assessed in those followed longitudinally. RESULTS: Participants were of low socioeconomic status (85% annual income < $15,000) and predominantly nonwhite (75%). Many (70%) scored less than 25 on the MoCA; those with a low MoCA score were more likely to be nonwhite (81% vs 62%, p<.001) and report fair or poor health (54% vs 41%, p=.04). Over 6 months, a MoCA score of less than 25 was associated with multiple emergency department visits (32% vs 13%, p=.02), hospitalization (35% vs 16%, p=.03), and repeat arrests (45% vs 21%, p=.01). CONCLUSIONS: Cognitive impairment is prevalent in older adults in jail and is associated with adverse health and criminal justice outcomes. A geriatric approach to jail-based and transitional health care should be developed to assess and address cognitive impairment. Additional research is needed to better assess cognitive impairment and its consequences in this population. J Am Geriatr Soc 66:2065-2071, 2018.
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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 | 0.003 |
| 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 | 0.001 | 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".