Incident Functional Limitations Among Older Adults With Diabetes During the COVID-19 Pandemic: An Analysis of Prospective Data From the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were 1) to examine and compare changes in functional limitations during the COVID-19 pandemic among older adults with and without diabetes; and 2): to identify key risk factors associated with developing functional limitations among older adults with and without diabetes during the pandemic. METHODS: We analyzed data collected from the Canadian Longitudinal Study on Aging. The analysis was restricted to those with no functional limitations in the follow-up 1 wave (2015 to 2018) (final sample N=6,045). Regression models were used to describe associations between diabetes status and functional limitation outcomes. We conducted stratified analyses to evaluate whether these associations varied by sociodemographic indicators. We also predicted the probability of the development of ≥1 functional limitation among those with and without diabetes for various patient profiles. RESULTS: Older adults with diabetes were 1.28-fold (95% confidence interval 1.02 to 1.60) more likely to develop ≥1 functional limitation than older adults without diabetes after controlling for relevant sociodemographic and health covariates. Risk factors for incident functional limitations among older adults, both with and without diabetes, include increasing age, low socioeconomic status, obesity, multimorbidity, and physical inactivity. CONCLUSIONS: Our findings indicate that older adults with diabetes were at an increased risk of developing functional limitations during the pandemic when compared with older adults without diabetes, even when controlling for several key risk factors. Targetting modifiable risk factors, such as physical activity, may help to reduce the risk of functional limitations among older adults with diabetes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".