Understanding Determinants of Sustainability Through a Realist Investigation of a Large‐Scale Quality Improvement Initiative (Lean): A Refined Program Theory
Bibliographic record
Abstract
BACKGROUND: Implementation science research seeks to understand ways to best ensure uptake of research-based initiatives to health care; however, there is little research done on how to sustain such efforts. Sustainability is the degree to which an initiative continues to be used in practice after efforts of implementation have ended. Sustainability research is a growing field of implementation science that needs further research to understand how to predict and measure the long-term use of effective initiatives to improve health care. The question of what influences the sustainability of research-based initiatives to improve health care remains unknown. PURPOSE: The purpose of this article was to present a refined program theory on the contextual factors and mechanisms that influence the sustainability of one large-scale quality management initiative (Lean) in pediatric health care. DESIGN: We conducted a multiphase realist investigation to explain under what contexts, for whom, how, and why Lean efforts are sustained or not sustained in pediatric health care through the generation of an explanatory program theory. METHODS: This article presents the theoretical triangulation of our multiphase realist investigation, resulting in a refined program theory. We integrated the initial program theories (IPTs) from each research phase to form a refined program theory. It involved going back and forth from the initial IPT to the findings from each phase and our middle-range theories and examining the most substantiated IPTs on the contextual factors and mechanisms that influenced the sustainability of Lean efforts. FINDINGS: The refined program theory depicts the complex nature to sustaining Lean efforts and that sustainability as a small, often unrepresentative portion of something much larger or more complex that cannot yet be seen or understood. The approach and nature of implementation is critical to shaping contexts for sustainability. Outcomes from implementation become facilitating or hindering contexts for sustainability. Customization to context is an important contextual factor for sustainability. Sense making, value congruency, and staff engagement are critical aspects from early implementation that enable or hinder processes of sustainment. Such mechanisms can trigger staff empowerment that can lead to a greater likelihood of sustainability. CONCLUSIONS: These findings have important implications for sustainability research, in understanding the determinants of sustainability of research-based initiatives in health care. CLINICAL RELEVANCE: It is important to understand and explain determinants of sustainability through theory-driven evaluative research in order to assist key stakeholders in sustaining the effective research-based initiatives made to improve healthcare services, patient care, and 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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".