Recent innovations in long-term care coverage and financing: a rapid scoping review
Bibliographic record
Abstract
OBJECTIVES: To identify, chart and analyse the literature on recent initiatives to improve long-term care (LTC) coverage, financial protection and financial sustainability for persons aged 60 and older. DESIGN: Rapid scoping review. DATA SOURCES: Four databases and four sources of grey literature were searched for reports published between 2017 and 2022. After using a supervised machine learning tool to rank titles and abstracts, two reviewers independently screened sources against inclusion criteria. ELIGIBILITY CRITERIA: Studies published from 2017-2022 in any language that captured recent LTC initiatives for people aged 60 and older, involved evaluation and directly addressed financing were included. DATA EXTRACTION AND ANALYSIS: Data were extracted using a form designed to answer the review questions and analysed using descriptive qualitative content analysis, with data categorised according to a prespecified framework to capture the outcomes of interest. RESULTS: Of 24 reports, 22 were published in peer-reviewed journals, and two were grey literature sources. Study designs included quasi-experimental study, policy analysis or comparison, qualitative description, comparative case study, cross-sectional study, systematic literature review, economic evaluation and survey. Studies addressed coverage based on the level of disability, income, rural/urban residence, employment and citizenship. Studies also addressed financial protection, including out-of-pocket (OOP) expenditures, copayments and risk of poverty related to costs of care. The reports addressed challenges to financial sustainability such as lack of service coordination and system integration, insufficient economic development and inadequate funding models. CONCLUSIONS: Initiatives where LTC insurance is mandatory and accompanied by commensurate funding are situated to facilitate ageing in place. Efforts to expand population coverage are common across the initiatives, with the potential for wider economic benefits. Initiatives that enable older people to access the services needed while avoiding OOP-induced poverty contribute to improved health and well-being. Preserving health in older people longer may alleviate downstream costs and contribute to financial sustainability.
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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 | 0.047 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.035 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".