Toward the sustainability of health care innovations to “transform our world”: current status and the road ahead
Bibliographic record
Abstract
ABSTRACT: Inadequate sustainability of health care innovations or evidence-based interventions has led to calls from policymakers, researchers, and funders for research on how sustainability can be optimized to avoid research waste. In this discussion paper, we argue that research on health care innovation sustainability needs to be advanced. We critically examine the literature on the concept of sustainability and propose that research should address the fundamental question: How can we advance knowledge on health care innovation sustainability? We provide examples of important work undertaken in the field of implementation science, including definitions and conceptualizations of sustainability. We also highlight theories, models, and frameworks that have been proposed to inform sustainability research and guide how to plan for sustainability. Our analysis of the literature reveals a growing interest in the sustainability of health care innovations but also confirms that implementation science has yet to put sustainability at the center of its research endeavors. To assist this shift, we identify priority research gaps and use the United Nations 2030 Agenda for Sustainable Development as a road map for an implementation science research agenda to drive health care innovation sustainability. We propose three new research directions that, overall, aim for "better health for all, leaving no one behind." These directions include: (1) advancing substantive research on sustainability while avoiding duplication; (2) identifying barriers, facilitators, and strategies to sustain engagement with multiple partners; and (3) advancing methods and tools to support monitoring, evaluation, and revision of strategies over time. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A323.
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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 | 0.094 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.030 | 0.036 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 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; 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".