Medical commitment to Lean: an inductive model development
Bibliographic record
Abstract
Purpose The purpose of this paper is to study the factors influencing doctors' involvement in Lean change initiatives in public healthcare organizations in Canada. Design/methodology/approach An inductive research was conducted over a three-year span studying Lean implementation across three healthcare organizations in Canada. Various interviews were conducted with healthcare actors. Through analytical induction, analysis of the data allowed for multiple factors to be triangulated from which a conceptual model was developed. Findings Fifty-four interviews with 18 Lean healthcare actors allowed for the identification of ten factors possibly influencing the commitment of doctors towards Lean change. These factors are categorized into pre-change antecedents and change antecedents. Also, the level of transformational leadership demonstrated by a project manager was shown to potentially moderate the effect of medical behavioral support for change on change outcomes. These findings allowed us to develop a conceptual model of medical commitment and its impact of Lean change outcomes. Originality/value The paper investigates the role doctors play in Lean implementation, currently an important issue discussed among healthcare actors and researchers. Yet, very little academic research has been published on this subject.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".