The Efficacy Implementation Ratio: A Conceptual Model for Understanding the Impact of Implementation Strategies Using Health Outcomes
Bibliographic record
Abstract
Improved health outcomes are the standard by which the benefit and value of implementation should be judged. Implementation outcomes are typically used to provide important measurements of processes and inputs in implementation science. However, it cannot be assumed that changes in implementation outcomes will always translate to health outcome improvements. Health outcomes are influenced by both the efficacy of treatments as well as how well they are implemented, so determining the success of implementation strategies using health outcomes may be influenced by the efficacy of treatments being integrated to practice. It is important to account for this variation in treatment efficacy when ascertaining the relative contribution of an implementation strategy to improved health outcomes. We propose a conceptual model to illustrate this issue, which considers the success of an implementation strategy, relative to the efficacy of the treatment being implemented, to evaluate the indirect success of implementation strategies on health outcomes. This is observed using an efficacy implementation ratio ( $${\text{EIR}}$$ ), expressed as a ratio of the impact of treatments promoted by an implementation strategy ( $${\text{ab}}$$ ) and that of the treatment in isolation ( $${\text{b}}$$ ): $${\text{EIR}}=\frac{{\mu }_{\text{ab}}}{{\mu }_{\text{b}}}$$ . Considering the indirect impact of implementation strategies on health outcomes, relative to the efficacy of implemented treatments provides a potential way to account for variations in treatment efficacy when ascertaining the success, benefits, and value of an implementation strategy in a given context. This paper proposes a novel conceptual model to reason and communicate our argument that the efficacy of treatment needs to be better considered during implementation evaluations.
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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 | 0.053 | 0.075 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".