Using decision methods to examine the potential impact of intersectoral action programs
Bibliographic record
Abstract
OBJECTIVES: In public health today, there is a widespread call for intersectoral action (ISA) programs, in which two or more sectors cooperate to address a problem. This trend raises a question of how to appropriately assess the effectiveness and cost-effectiveness of ISA programs. To assess the impact of ISA, evaluation methods should provide a framework for simultaneously considering the impact of two or more interventions when selecting from a portfolio of programs. There is a gap in literature on such methods. In this research note, from a narrative review, we report and describe methods that could be useful for evaluating ISA programs. Subsequently, we present a hypothetical case study to demonstrate the use of these methods. RESULTS: We identified four methods that have potential to assess the joint impact of multiple interventions: economic evaluation, portfolio analysis, multiple-criteria decision analysis, and programme budgeting and marginal analysis. To keep pace with the desire to use strong evidence to inform the selection and design of ISA programs, methods must evolve to support these initiatives. This research note seeks to begin a dialogue on existing decision methods which may be used to assist decision makers with funding and resource allocation decisions of ISA programs.
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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 | 0.138 | 0.199 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".