Financing networks of care: a cross-case analysis from six countries
Bibliographic record
Abstract
OBJECTIVES: Describe experiences of countries with networks of care's (NOCs') financial arrangements, identifying elements, strategies and patterns. DESIGN: Descriptive using a modified cross-case analysis, focusing on each network's financing functions (collecting resources, pooling and purchasing). SETTING: Health systems in six countries: Argentina, Australia, Canada, Singapore, the United Kingdom and the USA. PARTICIPANTS: Large-scale NOCs. RESULTS: Countries differ in their strategies to implement and finance NOCs. Two broad models were identified in the six cases: top-down (funding centrally designed networks) and bottom-up (financing individual projects) networks. Despite their differences, NOCs share the goal of improving health outcomes, mainly through the coordination of providers in the system; these results are achieved by devoting extra resources to the system, including incentives for network formation and sustainability, providing extra services and setting incentive systems for improving the providers' performance. CONCLUSIONS: Results highlight the need to better understand the financial implications and alternatives for designing and implementing NOCs, particularly as a strategy to promote better health in low- and middle-income settings.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".