Integrating cardiovascular implementation science research within healthcare systems
Bibliographic record
Abstract
Only 1 in 5 evidence-based interventions make it to routine clinical practice and the evidence generated from clinical research may take 17 years to be implemented. This represents a lost opportunity to improve clinical care in healthcare systems. Implementation science refers to the study of methods to promote the adoption and integration of evidence-based practices, interventions, and policies into real-world clinical settings to positively impact population health. Therefore, implementation roadmaps can be crucial for learning healthcare systems (LHS) to bridge the research-to-practice gap, particularly for cardiovascular disease which remains the leading cause of death in the United States. Implementation models exist, all of which require a thorough understanding of the key phases of implementation for effective healthcare system incorporation and optimization (pre-implementation, implementation, monitoring the implementation, evaluation, sustaining, and scaling-up or de-implementation). This review serves as a call-to-action for involvement of large-scale LHS for cardiovascular implementation science, and provides a roadmap by summarizing various implementation science models, highlighting key implementation phases and discussing successful initiatives to improve the process. We also assess challenges associated with implementation science and provide possible solutions to improve translation of evidence in real-world clinical settings.
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 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 | 0.090 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads 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".