Mapping variability in allocation of Long-Term Care funds across payer agencies in OECD countries
Bibliographic record
Abstract
INTRODUCTION: Long-term care (LTC) is organized in a fragmented manner. Payer agencies (PA) receive LTC funds from the agency collecting funds, and commission services. Yet, distributional equity (DE) across PAs, a precondition to geographical equity of access to LTC, has received limited attention. We conceptualize that LTC systems promote DE when they are designed to set eligibility criteria nationally (vs. locally); and to distribute funds among PAs based on needs-formula (vs. past-budgets or government decisions). OBJECTIVES: This cross-country study highlights to what extent different LTC systems are designed to promote DE across PAs, and the parameters used in allocation formulae. METHODS: Qualitative data were collected through a questionnaire filled by experts from 17 OECD countries. RESULTS: 11 out of 25 LTC systems analyzed, fully meet DE as we defined. 5 systems which give high autonomy to PAs have designs with low levels of DE; while nine systems partially promote DE. Allocation formulae vary in their complexity as some systems use simple demographic parameters while others apply socio-economic status, disability, and LTC cost variations. DISCUSSION AND CONCLUSIONS: A minority of LTC systems fully meet DE, which is only one of the criteria in allocation of LTC resources. Some systems prefer local priority-setting and governance over DE. Countries that value DE should harmonize the eligibility criteria at the national level and allocate funds according to needs across regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".