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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".