Why the Elderly Could Bankrupt Canada and How Demographic Imperatives Will Force the Redesign of Acute Care Service Delivery
Bibliographic record
Abstract
Canada's aging population poses a significant challenge for the existing healthcare system. While individuals 65 and older accounted for 13.7% of the population in 2005, they accounted for 60% of all acute care service spending. This paper further illustrates how the heterogeneity of the older population and its impact on patterns of healthcare use demonstrate the failings of our current care systems. Our outdated acute care models frequently disadvantage the system's highest users, who are often characterized by factors such as poly-morbidity, functional impairment and social frailty. Understanding how implementing innovative models that challenge deeply ingrained ways of providing care has proven to be a significant challenge, this paper highlights one hospital's mission to transform current traditional paradigms of care by developing and implementing an elder-friendly hospital integrated service delivery model. This hospital aims to demonstrate wide-ranging benefits of this model that can contribute toward optimizing the outcomes of hospitalization for older adults and the system as a whole. The establishment of a national agency that could support the development of a national aging strategy to promote best practice dissemination and implementation could also ensure that the significant health, social and economic benefits that better care models can realize could be more easily achieved.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.028 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 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".