Population attributable risk for functional disability associated with chronic conditions in Canadian older adults
Bibliographic record
Abstract
OBJECTIVES: to investigate the population impact on functional disability of chronic conditions individually and in combination. METHODS: data from 9,008 community-dwelling individuals aged 65 and older from the Canadian Study of Health and Aging (CSHA) were used to estimate the population attributable risk (PAR) for chronic conditions after adjusting for confounding variables. Functional disability was measured using activity of daily living (ADL) and instrumental activity of daily living (IADL). RESULTS: five chronic conditions (foot problems, arthritis, cognitive impairment, heart problems and vision) made the largest contribution to ADL- and IADL-related functional disabilities. There was variation in magnitude and ranking of population attributable risk (PAR) by age, sex and definition of disability. All chronic conditions taken simultaneously accounted for about 66% of the ADL-related disability and almost 50% of the IADL-related disability. CONCLUSIONS: in community-dwelling older adults, foot problems, arthritis, cognitive impairment, heart problems and vision were the major determinants of disability. Attempts to reduce disability burden in older Canadians should target these chronic conditions; however, preventive interventions will be most efficient if they recognize the differences in the drivers of PAR by sex, age group and type of functional disability being targeted.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".