CANADIAN EXPERTISE IN AGING, A SUPPLY CHAIN MANAGEMENT ISSUE?
Bibliographic record
Abstract
Like most western countries, Canada is undergoing a steep demographic shift, despite attempts to mitigate this issue through increased immigration. This demographic shift can be shown by changes in the median age of Canadians. In 1956, the median age in Canada was 27.2 years, climbing to 39.5 in 2006 and is expected to reach 46.9 by 2056. Such large demographic shifts make it difficult to meet the demand for both academic and clinical gerontology expertise. Although difficult to quantify, the current supply of aging experts is sorely lacking. A national effort has been expended to increase training, but the distribution of such highly recruited experts has become quite uneven. For example, a single hospital in Vancouver has 9 geriatric medicine specialists, while there is only one geriatrician in the entire province of Saskatchewan. Future workforce planning strategies need to focus both on distribution and quantity of expert personnel.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".