Implementation Research to Address the United States Health Disadvantage: Report of a National Heart, Lung, and Blood Institute Workshop
Bibliographic record
Abstract
Four decades ago, U.S. life expectancy was within the same range as other high-income peer countries. However, during the past decades, the United States has fared worse in many key health domains resulting in shorter life expectancy and poorer health-a health disadvantage. The National Heart, Lung, and Blood Institute convened a panel of national and international health experts and stakeholders for a Think Tank meeting to explore the U.S. health disadvantage and to seek specific recommendations for implementation research opportunities for heart, lung, blood, and sleep disorders. Recommendations for National Heart, Lung, and Blood Institute consideration were made in several areas including understanding the drivers of the disadvantage, identifying potential solutions, creating strategic partnerships with common goals, and finally enhancing and fostering a research workforce for implementation research. Key recommendations included exploring why the United States is doing better for health indicators in a few areas compared with peer countries; targeting populations across the entire socioeconomic spectrum with interventions at all levels in order to prevent missing a substantial proportion of the disadvantage; assuring partnership have high-level goals that can create systemic change through collective impact; and finally, increasing opportunities for implementation research training to meet the current needs. Connecting with the research community at large and building on ongoing research efforts will be an important strategy. Broad partnerships and collaboration across the social, political, economic, and private sectors and all civil society will be critical-not only for implementation research but also for implementing the findings to have the desired population impact. Developing the relevant knowledge to tackle the U.S. health disadvantage is the necessary first step to improve U.S. health outcomes.
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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 | 0.295 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.012 | 0.022 |
| Research integrity | 0.025 | 0.033 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".