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 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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".