Nutrition, Immigration and Health Determinants are Linked to Verbal Fluency among Anglophone Adults in the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
OBJECTIVES: Later-life cognitive impairment is an important health issue; however, little is known about the condition among diverse groups such as immigrants. This study aims to examine whether the healthy immigrant effect exists for verbal fluency, an indicator of cognitive functioning, among anglophone middle-aged and older adults in Canada. METHODS: Using from the baseline data of the Canadian Longitudinal Study on Aging (CLSA), multiple linear regression was employed to compare associations among immigrants (recent and long-term) and Canadian-born residents without dementia for two verbal fluency tests, the Controlled Oral Word Association Test (COWAT) and the Animal Fluency (AF) task. Covariates included socioeconomic, physical health, and dietary intake. RESULTS: Of 8,574 anglophone participants (85.7% Canada-born, 74.8% aged 45-65 years, 81.8% married, 81.9% with a post-secondary degree), long-term immigrants (settled in Canada >20 years) performed significantly better than Canadian-born residents for the COWAT (42.8 vs 40.9) but not the AF task (22.4 vs 22.4). Results of the multivariable adjusted regression analyses showed that long-term immigrants performed better than Canadian-born peers in both the COWAT (B=1.57, 95% CI: 0.80-2.34) and the AF test (B=0.57, 95% CI: 0.19-0.95), but this advantage was not observed among recent immigrants. Other factors associated with low verbal fluency performance included being single, socioeconomically disadvantaged, having hypertension, excess body fat, and consuming low amounts of pulses/nuts or fruit/vegetables. CONCLUSIONS: Long-term immigrants had higher verbal fluency test scores than their Canadian-born counterparts. Immigration status, social, health and nutritional factors are important considerations for possible intervention and prevention strategies for cognitive impairment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".