Growing Older Together: A Brief Report Comparing the Long-Term Care Systems in Australia and Canada
Bibliographic record
Abstract
Context: Australia and Canada are both currently working to improve their long-term care systems to, respectively, meet the growing needs of their ageing populations. Perspective: International long-term care system comparisons between similar countries can provide insights relevant to the development of long-term care policies and reforms that may improve the lives of older persons. From September 2022 to May 2023, we conducted an environmental scan of publicly available literature, comparing key elements of the long-term care systems in Australia and Canada. While both countries offer similar universal, publicly funded long-term care services, their organisational and governance structures differ significantly. Australia relies more heavily on residential care, whereas Canada has a stronger emphasis on in-home care services. Both countries face ongoing challenges related to the sustainability of their long-term care workforces and support for carers. Implications: The implications of this analysis suggest that both Australia and Canada can learn from each other’s best practices to enhance their long-term care systems. These insights have significant implications for long-term care practice, policy and future research, emphasising the need for sustainable workforce strategies, improved in-home care services and better support systems for carers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".