A realist evaluation to identify contexts and mechanisms that enabled and hindered implementation and had an effect on sustainability of a lean intervention in pediatric healthcare
Bibliographic record
Abstract
BACKGROUND: In 2012, the Saskatchewan Ministry for Health mandated a system-wide Lean transformation. Research has been conducted on the implementation processes of this system-wide Lean implementation. However, no research has been done on the sustainability of these Lean efforts. We conducted a realist evaluation on the sustainability of Lean in pediatric healthcare. We used the context (C) + mechanism (M) = outcome (O) configurations (CMOcs) heuristic to explain under what contexts, for whom, how and why Lean efforts are sustained or not sustained in pediatric healthcare. METHODS: We employed a case study research design. Guided by a realist evaluation framework, we conducted qualitative realist interviews with various stakeholder groups across four pediatric hospital units 'cases' at one acute hospital. Interview data was analyzed using an integrated approach of CMOc categorization coding, CMOc connecting and pattern matching. RESULTS: We conducted thirty-two interviews across the four cases. Five CMOcs emerged from our realist interview data. These configurations illustrated a 'ripple-effect' from implementation outcomes to contexts for sustainability. Sense-making and staff engagement were prominent mechanisms to the sustainment of Lean efforts. Failure to trigger these mechanisms resulted in resistance. The implementation approach used influenced mechanisms and outcomes for sustainability, more so than Lean itself. Specifically, the language, messaging and training approaches used triggered mechanisms of innovation fatigue, poor 'sense-making' and a lack of engagement for frontline staff. The mandated, top-down, externally led nature of implementation and lack of customization to context served as potential pitfalls. Overall, there was variation between leadership and frontline staff's perceptions on how embedded Lean was in their contexts, and the degree to which participants supported Lean sustainability. CONCLUSIONS: This research illuminates important contextual factors and mechanisms to the process of Lean sustainment that can be applicable to those implementing systems changes. Future work is needed to continue to develop the science on the sustainability of interventions for healthcare improvement.
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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 | 0.083 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".