Long-Term Care Models in Select OECD Countries and Policy Implications for Canada: A Focused Qualitative Systematic Review
Bibliographic record
Abstract
The COVID-19 pandemic highlighted many problems with Canada's older adults (OA) long-term care (LTC) model. The demographic changes in the next two decades require a novel approach to LTC. This study aimed to conduct a focused qualitative systematic review (SR) of the publicly supported LTC models and policies in select advanced economies. The authors used PubMed, Embase, and Medline to conduct an SR following the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) 2020 guidelines. Fully published articles in the English language related to LTC for Germany, Sweden, Australia, Denmark, France, and the Netherlands were included. Predefined data on the LTC models, including eligibility criteria, coverage, funding, and delivery methods, were extracted. Out of 1,682 screened articles/websites, 28 publications, websites, and reports were included. Despite differences in LTC models, there were two primary funding sources for LTC in the selected countries: general tax and LTC insurance. Aligned with the OAs preference, there was an emphasis on providing LTC at home. The care services were need-based and often defined by healthcare professionals or specialized teams. To address the growing number of OAs and to fulfill their needs, the Canadian LTC system requires a major shift to LTC at home and keeping the institutional LTC as the last resource. A sustainable LTC at home also requires a new legislative framework and financial levers.
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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 | 0.063 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.032 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".