How vulnerable are care systems to future changes in demand and supply? Providing a framework to compare Austria, Spain, UK and Canada
Bibliographic record
Abstract
This paper examines the evolving landscape of long-term care (LTC) provision in Austria, Spain, UK and Canada, four countries included in the collaborative research project WellCARE. Its aim is to provide a basis to understand the features and vulnerabilities of different care systems, highlighting the mechanisms affecting how and to what extent the demand for care is met today, and identifying the salient issues that will have to be addressed in the future. In the first part, we give an overview of different care regime classifications to provide the analytical framework for comparing and identifying the relevant traits of care systems as well as their trajectories over time. In the second part, we analyse the current care systems in the four countries in greater detail, using recent data covering a broad range of dimensions. Particular attention is paid to analysing how different factors influence the size and composition of the caregiving groups in society. Our analysis reveals the critical role of informal care in all countries, underscored by societal changes such as higher female labour force participation and declining fertility rates. While varying degrees of decommodification characterise LTC systems, all nations grapple with challenges of supply shortages and lengthy waiting lists, particularly in Canada and Spain. Microsimulation modelling is identified as a valuable tool for projecting future LTC demands and assessing policy interventions, accounting for demographic shifts, changing morbidity patterns, and social dynamics.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".