Using Microsimulation to Reassess Aging Trends in Canada
Bibliographic record
Abstract
Population aging is the population issue of the XXI century and many indices are used to measure its level and pace. In Science (2010), Sanderson and Scherbov suggested improvements to the measure of elderly dependency ratio. They identified several limitations to the use of chronological age as the main variable and proposed a new index, the Adult Disability Dependency Ratio, defined as the number of adults at least 20 years old with disabilities divided by the number of similarly aged adults without disabilities. They used the Sullivan prevalence-based method by multiplying derived disability rates to macro population projections. They showed results for several ECE and OECD countries; results for Canada (see online annex, available at https://www.sciencemag.org/content/329/5997/1287/suppl/DC1) were derived using coefficients of Italy. However, disability is a complex multidimensional process (see Carrière, Keefe, Légaré, Lin, & Rowe, 2007; Légaré and Décarie, 2011), and microsimulation can take into account its implied complexity. Our results for Canada, presented here, exceed those in Science to show how more-sophisticated projections of disabled older adults can improve the analysis. We used LifePaths, a Statistics Canada's microsimulation model, to provide a perspective of the phenomena unobtainable with prevalence-based methods.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".