Priority Setting in the 'Real-World' - from Principles to Processes and Performance
Bibliographic record
Abstract
Economics should provide the theory and solutions to help healthcare organizations make priority setting decisions. However, the impact of economics and health economic methods in particular on such decision-making is not clear. This presentation draws together research conducted in the England, Canada and Australia which sought to address how priority setting is understood in principle by decision-makers (i.e. how it ought to be undertaken) and how this translates in practice (i.e. how it is undertaken). Semi-structured interviews were conducted with key decision-makers in local/regional healthcare organizations in each country (>100). The transcripts formed the formal data for analysis and qualitative methods were used to identify broad themes and sub-themes. The results for each country as well as common themes across these countries are presented. These describe the context of priority setting in terms of: the guiding principles - there seems to be a general understanding and acceptance of resources scarcity and the need to make choices. In some cases decision makers can articulate specific criteria by which this should be done. the driving processes - priority setting decisions seem to be driven largely by history and politics. The use of evidence based approaches is less developed. the resulting performance - priority setting decisions do not seem to be made in an explicit, transparent, or accountable way. Organizations and individuals find it difficult to 'manage' priority setting decisions due to a lack of capacity, capability, and support. We discuss the implications of these results in terms of developing health economic methods for priority setting.
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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 | 0.146 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.031 | 0.034 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".