Exploring lean generic and lean healthcare cultural clusters
Bibliographic record
Abstract
Purpose Lean culture has been noted to be an underdeveloped concept. The purpose of this paper is to increase the understanding of Lean culture by determining its leading cultural clusters. Design/methodology/approach Content analysis was used to perform top relevant keywords exploration and qualitative analysis on main text of 33 reference books, 21 Lean generic and 12 Lean healthcare, consolidated as three cases (Lean general, Lean Liker et al. and Lean healthcare). Findings Four emergent Lean’s leading cultural clusters: operations, change, collectivity and humanity were identified inductively from ten 10 relevant keywords, namely, in order of importance: work, time, process, Lean, system, improvement, production, patient, people and team. Saliency of the word “time” is noteworthy. Cross-validation of these cultural clusters is demonstrated through sociotechnical systems theory. Research limitations/implications Content analysis is shown to be an effective research method enabling inductive analysis. Identification of four leading clusters should support productive further research on Lean culture. Practical implications The four cultural clusters indicate to healthcare and other domains managers, who wish to improve their Lean cultural transformation success rate, to focus their attention to what their organization actually does (operations), to how improvement happens (change) and to how everything (collectivity) and everyone (humanity) work together in their organization. Originality/value This work applies innovative content analysis on Lean reference books. It highlights the importance of time as an underappreciated Lean culture element. It provides evidence and additional support for link between Lean and sociotechnical systems theory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".