The Use of Contribution Analysis in Evaluating Health Interventions: A Scoping Review
Bibliographic record
Abstract
Contribution Analysis (CA) is a promising theory-based evaluation approach for complex interventions, yet its application in health interventions remains largely unexplored. To bridge this gap, we conducted a scoping review to examine the extent of such applications and the methodologies, strengths, and limitations of this approach in health programming. Our comprehensive search strategy was developed and used in 15 databases to identify peer-reviewed articles from 1999 to 2023 that focused on using CA to evaluate health interventions. We then implemented rigorous double- and triple-screening processes for abstracts and full-text papers, respectively. Data were extracted and narratively summarized. Our review found seven relevant studies, which showed that CA has been employed in health promotion programs, health policies, and targeted health issues such as nutrition, cardiovascular disease, substance misuse, and suicide prevention. The studies identified strengths of using CA, including its flexible impact evaluation approach, capacity to inform decision-making, and potential to enhance understanding of health programs and policies. However, challenges such as how to determine suitable evidence levels and how to best manage resource intensity were also identified. The limited number of studies indicates that CA is still a novel approach, whereas the variation in the reporting of the studies suggests that this approach could benefit from more standardized methods and detailed stakeholder engagement strategies.
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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 | 0.329 | 0.542 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.059 | 0.045 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".