Functional status in rural and urban adults: The Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
PURPOSE: To document the prevalence of functional impairment in middle-aged and older adults from rural regions and to determine urban-rural differences. METHODS: We have conducted a secondary analysis using data from an ongoing population-based cohort study, the Canadian Longitudinal Study on Aging (CLSA). We used a cross-sectional sample from the baseline wave of the "tracking cohort." The definition of rurality was the same as the one used in the CLSA sampling frame and based on the 2006 census. This definition includes rural areas, defined as all territory lying outside of population centers, and population centers, which collectively cover all of Canada. We grouped these into "Urban," "Peri-urban," "Mixed" (areas with both rural and urban areas), and "Rural," and compared functional status across these groups. Functional status was measured using the Older Americans Resource Survey (OARS) and categorized as not impaired versus having any functional impairment. Logistic regression models were constructed for the outcome of functional status and adjusted for covariates. FINDINGS: No differences were found in functional status between those living in rural, mixed, peri-urban, and urban areas in unadjusted analyses and in analyses adjusting for sociodemographic and health-related factors. There were no rural-urban differences in any of the individual items on the OARS scales. CONCLUSIONS: We found no rural-urban differences in functional status.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".