Inequalities in health-related quality of life and functional health of an aging population: A Canadian community perspective
Bibliographic record
Abstract
BACKGROUND: Reducing health inequalities among older adults is crucial to ensuring healthy aging is within reach for all. The current study provides a timely update on demographic- and geographic-related inequalities in healthy aging among older adults residing in Canadian communities. METHODS: Data was extracted from the Canadian Health Survey on Seniors [2019-2020] for ~6 million adults aged 65 years and older residing in 10 provinces of Canada. Healthy aging was defined by two indices: 1] health-related quality of life and 2] functional health. Poisson regression models and spatial mapping were used to demonstrate inequalities among age, race, and sex categories, and health regions. RESULTS: Approximately 90.3% of individuals reported less than perfect quality of life and 18.8% reported less than perfect functional health. The prevalence of less than perfect quality of life was higher for females [PR 1.14, 95% CI;1.02-1.29] and for older adults aged ≥80 years as compared to males and older adults aged ≤79 years [PR 1.66, 95% CI;1.49-1.85]. Similarly, the prevalence of less than perfect functional health was higher for females [PR 1.58, 95% CI;1.32-1.89] and for older adults aged ≥80 years [PR 2.71, 95% CI;2.59-2.84]. Spatial mapping showed that regions of lower quality of life were concentrated in the Prairies and Western Ontario, whereas regions of higher quality of life were concentrated in Quebec. CONCLUSIONS: Amongst older individuals residing in Canadian communities, less than perfect quality of life and functional health is unequally distributed among females, older adults aged ≥80 years, and those residing in the Prairie regions specifically. Newer policy should focus on interventions targeted at these subpopulations to ensure that healthy aging in within reach for all Canadians.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".