Where is “policy” in dissemination and implementation science? Recommendations to advance theories, models, and frameworks: EPIS as a case example
Bibliographic record
Abstract
BACKGROUND: Implementation science aims to accelerate the public health impact of evidence-based interventions. However, implementation science has had too little focus on the role of health policy - and its inseparable politics, polity structures, and policymakers - in the implementation and sustainment of evidence-based healthcare. Policies can serve as determinants, implementation strategies, the evidence-based "thing" to be implemented, or another variable in the causal pathway to healthcare access, quality, and patient outcomes. Research describing the roles of policy in dissemination and implementation (D&I) efforts is needed to resolve persistent knowledge gaps about policymakers' evidence use, how evidence-based policies are implemented and sustained, and methods to de-implement policies that are ineffective or cause harm. Few D&I theories, models, or frameworks (TMF) explicitly guide researchers in conceptualizing where, how, and when policy should be empirically investigated. We conducted and reflected on the results of a scoping review to identify gaps of existing Exploration, Preparation, Implementation, and Sustainment (EPIS) framework-guided policy D&I studies. We argue that rather than creating new TMF, researchers should optimize existing TMF to examine policy's role in D&I. We describe six recommendations to help researchers optimize existing D&I TMF. Recommendations are applied to EPIS, as one example for advancing TMF for policy D&I. RECOMMENDATIONS: (1) Specify dimensions of a policy's function (policy goals, type, contexts, capital exchanged). (2) Specify dimensions of a policy's form (origin, structure, dynamism, outcomes). (3) Identify and define the nonlinear phases of policy D&I across outer and inner contexts. (4) Describe the temporal roles that stakeholders play in policy D&I over time. (5) Consider policy-relevant outer and inner context adaptations. (6) Identify and describe bridging factors necessary for policy D&I success. CONCLUSION: Researchers should use TMF to meaningfully conceptualize policy's role in D&I efforts to accelerate the public health impact of evidence-based policies or practices and de-implement ineffective and harmful policies. Applying these six recommendations to existing D&I TMF advances existing theoretical knowledge, especially EPIS application, rather than introducing new models. Using these recommendations will sensitize researchers to help them investigate the multifaceted roles policy can play within a causal pathway leading to D&I success.
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 | 0.293 | 0.480 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.025 | 0.061 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".