A-6 Cognitive Impairment and SES-Related Differences in Affective Traits
Bibliographic record
Abstract
Abstract Objective Recent research has suggested that positive affect (PA) and negative affect (NA) may be sensitive markers of early pathological changes in Alzheimer’s disease and mild cognitive impairment (MCI). However, mechanisms underlying this relationship are poorly understood and likely reflect diverse etiological factors. Method The present study aimed to determine whether individual differences in NA and PA were related to group differences in global cognition and socioeconomic status (SES). We collected data from a group of adult participants aged 57–87 (N = 120). Participants were categorized as cognitively normal (CN) or MCI based on Montreal Cognitive Assessment (MoCA) scores. The Positive and Negative Affect Schedule (PANAS) measured PA and NA. Information on SES including education level and household income were collected via interview. Results Results indicated that those with psychometrically-defined MCI statistically differed in PA but not in NA compared to CN adults. Additionally, economically insecure older adults were higher in NA than economically secure older adults. Further, examination of this effect suggested that this economically-related group difference was significantly greater in those with evidence of cognitive impairments than CN. Older age was associated with greater positive affect (r = 0.170, p < 0.050) and lower negative affect (r = −0.296, p = 0.001). Conclusions Results highlight the combined effect of economic insecurity and cognitive impairment on NA, suggesting that economically insecure adults with cognitive impairments may benefit from additional support to reduce its harmful effects on mood. Future research is needed to examine whether heightened NA is a sensitive predictor of depression and MCI.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".