COHORT EFFECTS IN DISABILITY: IMPLICATIONS FOR MORE DISABILITY IN OLD AGE AND IN RECENT GENERATIONS?
Bibliographic record
Abstract
The goal of this study was to determine a) if the age-trajectory (life course) of disability differs by birth cohort and b) whether any cohort differences are explained by changes in socio-economic status (SES), lifestyle factors, and the presence of chronic conditions. We used biannually collected data from the 1994–2010 Canadian Longitudinal National Population Health Survey: 10,330 participants born from 1925 to 1974 grouped in five 10-year birth cohorts. The outcome was reported disability (needing help with daily living activities or reporting long-term disability). We used multilevel logistic growth models to examine cohort effects in the age-trajectory of disability adjusting for sex, SES (education, income), lifestyle factors (BMI, physical activity, sedentary behavior, smoking status) and multimorbidity (2+ conditions up to 17). We found significant cohort differences in the age-trajectory of disability (p<0.0001): when compared at the same age, each succeeding recent cohort had higher odds of disability than those in the earlier cohort. The age-trajectories were similar for men and women, although women had higher prevalence of disability. Low SES (education and/or income), being smoker, obesity, and multimorbidity were associated with increased odds of having disability. Though attenuated, cohort differences remained significant after accounting for differences in SES, lifestyle factors, and multimorbidity. The results suggest that more recent cohorts of Canadian adults are more likely to have disability and that they report disability earlier than previous generations. This finding has important implications for the organization and planning of healthcare and social services for the disabled population.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".