What factors are considered in hospital funding models? A review of the literature on health services funding in organisation for economic co‐operation and development countries
Bibliographic record
Abstract
BACKGROUND: One of the most difficult challenges in healthcare involves equitable allocation of resources. Our review aimed to identify international funding models in Organisation for Economic Co-operation and Development (OECD) countries for government-funded public hospitals and evidence underpinning their efficacy, via review of the peer-reviewed and grey literature. METHODS: Ovid-Medline, Ovid Embase, Scopus, and PubMed were searched for peer-reviewed literature. Advanced Google searches and targeted hand searches of relevant organisational websites identified grey literature. Inclusion criteria were: English language, published between 2011 and 2022, and that the article: (1) focused on healthcare funding; (2) reported on or identified specific factors, indexes, algorithms or formulae associated with healthcare funding; and (3) referred to countries that are members of the OECD, excluding the United States (US). RESULTS: For peer-reviewed literature 1189 abstracts and 35 full-texts were reviewed; six articles met the inclusion criteria. For grey literature, 2996 titles or abstracts and 37 full-texts were reviewed; five articles met the inclusion criteria. Healthcare funding arrangements employed in 15 OECD countries (Australia, Belgium, Canada, Finland, France, Germany, Israel, Italy, the Netherlands, New Zealand, Norway, Spain, Sweden, Switzerland, and the United Kingdom [UK; specifically, England, Scotland, Wales and Northern Ireland]) were identified, but papers reported population-based funding arrangements for specific regions rather than hospital-specific models. CONCLUSIONS: While some models adjusted for deprivation and ethnicity factors, none of the identified documents reported on health systems that adjusted funding allocation for social determinants such as health literacy levels.
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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 | 0.034 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.033 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".