Demographic predictors of cognitive performance in participants of a local substance abuse recovery program
Bibliographic record
Abstract
Background Cognitive impairments have been reported among disadvantaged populations. Objective We aimed to ascertain how demographic factors are associated with cognitive performance in individuals enrolled in a local substance abuse recovery program. Methods In total, 106 participants were included in the study. Besides demographic information, vital signs and cognitive function, measured by Mini-Mental State Examination (MMSE) or Montreal Cognitive Assessment (MoCA), were collected from each participant. Welch's t-test and regression analysis were used to analyze how different demographic factors are associated with cognitive assessment scores. Results The mean age of African American (AA) participants (n = 43) were 48.35 ± 1.65 years, which are older than that for the White participants of 38.95 ± 1.36 (n = 63) years. Compared to the AA participants, the White participants had a larger variance in attained education levels. The average MMSE scores were 27.09 ± 0.40 for AA participants, which is lower than that for the White participants of 28.52 ± 0.33 ( p < 0.05). The average MoCA scores were 23.71 ± 0.54 for AAs, which is lower that for the White participants of 26.65 ± 0.44 ( p < 0.001). The AA and White participant groups had cognitive impairment rate of 18.6% and 6.35%, respectively. The regression analysis indicates age and education are two significant predictors for the cognitive performance difference between the two racial groups. Conclusions Significant disparities in cognitive performance exist between two racial groups of enrolled in a local substance abuse recovery program. The older age and lower levels of attained education in AA participants can explain the poorer cognitive function than the White participants.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".