Supporting successful implementation of public health interventions: protocol for a realist synthesis
Bibliographic record
Abstract
BACKGROUND: There is a growing emphasis in public health on the importance of evidence-based interventions to improve population health and reduce health inequities. Equally important is the need for knowledge about how to implement these interventions successfully. Yet, a gap remains between the development of evidence-based public health interventions and their successful implementation. Conventional systematic reviews have been conducted on effective implementation in health care, but few in public health, so their relevance to public health is unclear. In most reviews, stringent inclusion criteria have excluded entire bodies of evidence that may be relevant for policy makers, program planners, and practitioners to understand implementation in the unique public health context. Realist synthesis is a theory-driven methodology that draws on diverse data from different study designs to explain how and why observed outcomes occur in different contexts and thus may be more appropriate for public health. METHODS: This paper presents a realist review protocol to answer the research question: Why are some public health interventions successfully implemented and others not? Based on a review of implementation theories and frameworks, we developed an initial program theory, adapted for public health from the Consolidated Framework for Implementation Research, to explain the implementation outcomes of public health interventions within particular contexts. This will guide us through the review process, which comprises eight iterative steps based on established realist review guidelines and quality standards. We aim to refine this initial theory into a 'final' realist program theory that explains important context-mechanism-outcome configurations in the successful implementation of public health interventions. DISCUSSION: Developing new public health interventions is costly and policy windows that support their implementation can be short lived. Ineffective implementation wastes scarce resources and is neither affordable nor sustainable. Public health interventions that are not implemented will not have their intended effects on improving population health and promoting health equity. This synthesis will provide evidence to support effective implementation of public health interventions taking into account the variable context of interventions. A series of knowledge translation products specific to the needs of knowledge users will be developed to provide implementation support. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015030052.
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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 | 0.231 | 0.357 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.107 | 0.026 |
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".