Understanding Economic Evaluations: A Guide for Health and Human Services
Bibliographic record
Abstract
Abstract: Interest is growing in the use of economic methods to evaluate our investments in public and charitable sector programming. Calculating the costs and consequences of different interventions provides new and better information on the relative costeffectiveness of competing alternatives. More commonly used to consider and evaluate health options, economic evaluations are equally useful in prevention programs and community-based services. This article reviews the potential application of economic evaluations, describes the basic methodologies used, and discusses some of the best strategies for disseminating results. Providing a balanced perspective on the methodological limitations, the article encourages evaluators and program sponsors to carefully consider the use of economic evaluation in their field.
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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 | 0.118 | 0.214 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.048 | 0.020 |
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".