Measuring the Appropriate Outcomes for Better Decision-Making: A Framework to Guide the Analysis of Health Policy
Bibliographic record
Abstract
Many existing economic evaluations of health policy recognize multidimensional outcomes and the importance of equally distributing the benefits, but do not to incorporate all relevant outcomes into a single comprehensive metric for cost-benefit analysis. The Organization for Economic Co-operation and Development’s (OECD’s) inclusive growth framework offers a novel approach for improved evaluation of policies which can address these concerns by aggregating societal outcomes in terms of income, life expectancy, unemployment rates and inequality into a single measure of living standards. We discuss the inclusive growth framework in the context of health policy and how it can be utilized by business leaders and policymakers to make superior policy decisions. Using an inclusive growth index of living standards developed by the OECD, we decompose growth in living standards (as defined by the OECD) due to increased life expectancy in Canada between 2000 and 2011 by cause of death and estimate the equivalent value of these reductions in mortality in terms of billions of dollars of income. We discuss factors underlying these reductions in mortality and suggest how they have been linked to policy. This exercise illustrates one way in which the inclusive growth framework can be used to evaluate the impacts of health policy.
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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 | 0.133 | 0.140 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".