Ethics and economics: does programme budgeting and marginal analysis contribute to fair priority setting?
Bibliographic record
Abstract
OBJECTIVE: Limited resources mean that decision-makers must set priorities among competing opportunities. Programme budgeting and marginal analysis (PBMA) is an economic approach that focuses on optimizing benefits with available resources. Accountability for reasonableness (A4R) is an ethics approach that focuses on ensuring fair priority-setting processes. PBMA and A4R have been used separately to provide decision-makers with advice about how to set priorities within limited resources. The goals of this research were to use the A4R framework to evaluate the fairness of using PBMA for priority setting and to assess how A4R might make PBMA fairer. METHODS: Qualitative case studies to describe priority setting using PBMA in the Calgary Health Region (Alberta, Canada) evaluated using A4R as a conceptual framework. RESULTS: The use of PBMA for priority setting was fairer than previous priority setting because of its emphasis on explicit rational decision-making. However, there were opportunities to improve the process, particularly by collecting data related to the decision criteria, by developing a communication plan to engage internal and external stakeholders about priority-setting, and by providing a formal mechanism to review priority-setting decisions and resolve disputes. CONCLUSIONS: There is potential for combining A4R and PBMA in a more comprehensive approach to priority setting, which uses a fair priority-setting process to reach decisions aimed at achieving optimal benefits with available resources.
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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 | 0.121 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".