Moving beyond formulae: a review of international population-based resource allocation policy and implications for Ireland in an era of healthcare reform
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> Population-based resource allocation is a specific approach to population health planning that is used to address differences in population need to promote equity and efficiency in health and health system outcomes. However, while previous studies have <ns4:italic>described</ns4:italic> this type of funding model, they have not compared <ns4:italic>how</ns4:italic> such policies and practices have been implemented across jurisdictions. This research examined the impacts and outcomes of population-based resource allocation across six high-income countries, with a view to informing strategic decision-making as Ireland progresses its universal healthcare reform agenda. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> A concurrent multi-method approach was employed to examine the experiences of six jurisdictions selected for analysis: Australia (New South Wales), Canada (Alberta), England, New Zealand, Scotland and Sweden (Stockholm). A documentary analysis of key policy, strategy and planning publications was combined with a narrative rapid review of peer-reviewed and grey literature (n = 8) to determine how population-based resource allocation is specified and implemented. The findings were checked and verified by national experts. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> Notable differences were observed across countries in terms of the stated objectives and descriptions of models as well as the criteria for choosing variables and the variables ultimately used in funding formulae. While population-based resource allocation can help improve equity related to healthcare outcomes and access, a number of tensions were revealed between the need to ensure alignment between policy goals and model design; transition between models; support regionalisation policies; and develop robust governance and monitoring mechanisms to maximise outcomes. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> The review progresses ‘thinking’ about population-based resource allocation beyond the technical aspects of model or formulae construction. Population-based resource allocation should be viewed as just one lever of large-scale health system reform that can be thoughtfully developed, monitored and adjusted in a way that supports the goals of Sláintecare and the delivery of universal healthcare. </ns4:p>
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".