The right kind of smart: emotional intelligence’s relationship to cognitive status in community-dwelling older adults
Bibliographic record
Abstract
OBJECTIVES: To examine whether emotional intelligence (EI) is associated with cognitive function (CF) in a sample of community-dwelling, non-demented elderly out-patients. DESIGN: Correlational cross-sectional study. SETTING: Two memory clinics in an urban community in central Israel. PARTICIPANTS: Individuals age 60 and older without dementia, recruited from two memory clinics (N = 151). MEASUREMENTS: Health history was obtained from medical charts. All participants underwent tests measuring CF, basic and instrumental function, general mental ability (GMA), EI, and depression. RESULTS: Mean age of the participants was 79 years (SD = 7.00) with 96 females (63.6%). Mean score for Montreal Cognitive Assessment (MoCA) was 21.62 (SD = 3.09) and for EI was 14.08 (SD = 3.30). Linear multiple regression analysis was conducted to examine associations of CF with EI while controlling for gender, age, education, GMA, and Charlson Comorbidity Index (CCI). Age, education, GMA, and CCI were significant correlates of CF and accounted for 31.1% of the variance [F(7,143) = 10.8, p<0.01] in CF. EI was added in the second block and was the factor most strongly associated with CF, explaining an additional 9.1% (a total of 40.2%) of the variance in CF [F(8,142) = 13.2, p<0.01]. CONCLUSION: This study is the first to show the association between EI and CF in older adults. Future prospective studies are needed to explicate the possibility of EI as a protective factor against cognitive decline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".