THE RIGHT KIND OF SMART? EMOTIONAL INTELLIGENCE AND COGNITIVE IMPAIRMENT IN OLDER ADULTS
Bibliographic record
Abstract
Background: Health psychology, emphasizes the potential of individual resources in effectively adapting to health challenges. Older adults with cognitive impairment (CI) suffer depletion of individual resources (e.g.: intelligence, certain personality traits, etc.) that jeopardizes their ability to adapt and function. Emotional intelligence (EI), a relatively new concept, holds promise as a pivotal resource for health challenges that until recently went unnoticed. In this pilot study we examined the distribution and inter-correlations among individual resource measures against a cognitive impairment measure to test EI’s potential role as a resource for older adults coping with CI. Methods: Applying a correlational study design, 66 older adults (mean age 77.92 ± 6.83), 60% of whom were women, were recruited from a memory clinic in central Israel. The participants showed a broad spectrum of cognitive function on Montreal Cognitive Assessment (MoCA scores 15–30), ranging intact through Mild Cognitive Impairment to dementia. General intelligence (GI), EI, Instrumental ADL (IADL), social support and comorbidities were assessed. Results: EI positively associated with IADL (r=.37; p<.01) GI (r=.26; p<.05) and social support (r=.27; p<.05). In a regression analysis controlling for intervening factors EI did not correlate with MoCA while showing associations with GI (β=.26; p<.05), IADL (β=.21; p<.05) and education level (β=.42; p<.01). Conclusions: As GI decreases with CI, EI remains relatively intact as an individual resource. This first of its kind evidence supports the possibility that EI may serve as a resilient resource in older adults with CI that may facilitate coping with the challenges of this condition.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".