The use of mediation analysis in evaluation of complex health interventions
Bibliographic record
Abstract
This article presents an application of the causal inference approach to mediation analysis using the example of a complex intervention that aimed to improve the quality of care at health centres in Uganda. Mediation analysis is a statistical method that aims to isolate the causal mechanisms that make an intervention work in a given context. We combined data from a cluster randomized control trial and a mixed-methods process evaluation. We developed two causal models following our hypotheses of how the intervention was intended to work through mechanisms at health centres to improve health outcomes in the community. In adjusted analyses, there was evidence of an effect of the intervention on some health centre mechanisms; however, these did not lead to improvements in community health outcomes. We discuss the practical and epistemological challenges encountered when using mediation analysis to evaluate a complex intervention. These findings will inform future evaluations. Trial registration: The trial reported in this article is registered at: clinicaltrials.gov, NCT01024426. Registered 2 December 2009, https://clinicaltrials.gov/ct2/show/record/NCT01024426?term=NCT01024426&draw=2&rank=1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.594 | 0.751 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".