Defining sustainability in practice: views from implementing real-world innovations in health care
Bibliographic record
Abstract
BACKGROUND: One of the key conceptual challenges in advancing our understanding of how to more effectively sustain innovations in health care is the lack of clarity and agreement on what sustainability actually means. Several reviews have helped synthesize and clarify how researchers conceptualize and operationalize sustainability. In this study, we sought to identify how individuals who implement and/or sustain evidence-informed innovations in health care define sustainability. METHODS: We conducted in-depth, semi-structured interviews with implementation leaders and relevant staff involved in the implementation of evidence-based innovations relevant to cancer survivorship care (n = 27). An inductive approach, using constant comparative analysis, was used for analysis of interview transcripts and field notes. RESULTS: Participants described sustainability as an ongoing and dynamic process that incorporates three key concepts and four important conditions. The key concepts were: (1) continued capacity to deliver the innovation, (2) continued delivery of the innovation, and (3) continued receipt of benefits. The key conditions related to (2) and (3), and included: (2a) innovations must continue in the absence of the champion or person/team who introduced it and (3a) adaptation is critical to ensuring relevancy and fit, and thus to delivering the intended benefits. CONCLUSIONS: Participants provided a nuanced view of sustainability, with both continued delivery and continued benefits only relevant under certain conditions. The findings reveal the interconnected elements of what sustainability means in practice, providing a unique and important perspective to the academic literature.
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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 | 0.123 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.066 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".