Using contribution analysis to evaluate the impacts of research on policy: Getting to ‘good enough’
Bibliographic record
Abstract
Assessing societal impacts of research is more difficult than assessing advances in knowledge. Methods to evaluate research impact on policy processes and outcomes are especially underdeveloped, and are needed to optimize the influence of research on policy for addressing complex issues such as chronic diseases. Contribution analysis (CA), a theory-based approach to evaluation, holds promise under these conditions of complexity. Yet applications of CA for this purpose are limited, and methods are needed to strengthen contribution claims and ensure CA is practical to implement. This article reports the experience of a public health research center in Canada that applied CA to evaluate the impacts of its research on policy changes. The main goal was to experiment with methods that were relevant to CA objectives, sufficiently rigorous for making credible claims, and feasible. Methods were ‘good enough’ if they achieved all three attributes. Three cases on government policy in tobacco control were examined: creation of smoke-free multiunit dwellings, creation of smoke-free outdoor spaces, and regulation of flavored tobacco products. Getting to ‘good enough’ required careful selection of nested theories of change; strategic use of social science theories, as well as quantitative and qualitative data from diverse sources; and complementary methods to assemble and analyze evidence for testing the nested theories of change. Some methods reinforced existing good practice standards for CA, and others were adaptations or extensions of them. Our experience may inform efforts to influence policy with research, evaluate research impacts on policy using CA, and apply CA more broadly.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.733 | 0.821 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.034 | 0.047 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".