Long-Term Demographic Forecasts and Implications for Health Care Resources and Repurposing
Bibliographic record
Abstract
We examine long-term demographic forecasts to determine whether increases in the senior population will be followed by a decrease once the baby-boom generation passes. Planners may therefore need to flexibly assign resources to allow for future repurposing of investments. Forecasts in the U.S. and Canada indicate that the number of seniors in the population will plateau by the year 2045 with levels roughly maintained until at least 2060; thus, repurposing may be unnecessary. Increases in life-expectancy, immigration age structures, and echoes of the baby-boom generation in later years are expected to help maintain this plateau. While there is no observable decrease in the senior population by 2060, there is uncertainty around the expected rate of decline in health of this generation. Depending on this trajectory, community-level social supports could play a large role in maintaining senior health and independence as long as possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".