Causal mediation analysis: what is it and how can it be used to inform practice and policy?
Bibliographic record
Abstract
BACKGROUND: Causal mediation, a quantitative analysis method, has the potential to be a valuable addition to any primary care provider, researcher, or student's toolbox. OBJECTIVE: This manuscript describes the theory behind causal mediation, provides a running example to help understand the application of this method in research, and explains how the results may be applied practically to help design appropriate interventions. METHODS AND APPLICATION: Causal mediation allows an exploration of the mechanism of action of a primary care intervention on an outcome that may pass through a third variable that is on the causal pathway, a mediator. Causal mediation analysis allows the decomposition of the total effect of an intervention on an outcome into both direct and indirect effects. Careful interpretation of generated results can guide decision-makers when devising or refining interventions or policies that affect patient health outcomes in primary care. CONCLUSION: Causal mediation has been used in many disciplines and is well-positioned to answer varied research questions. However, the full extent of its potential has yet to be realized.
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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 | 0.464 | 0.607 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.020 | 0.035 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".