Lean in healthcare: Engagement in development, job satisfaction or exhaustion?
Bibliographic record
Abstract
Conclusions about implementing the management concept lean in healthcare are contradictory and longitudinal studies are scarce. In particular, little is known of how working conditions contribute to the sustainability of lean in healthcare. The aim of this article is to identify to what extent lean tools (visual follow-up boards, standardised work, 5S [housekeeping], and value stream mapping [VSM]) promote working conditions for employees and managers in healthcare organisations (outcomes: engagement in development, job satisfaction and exhaustion), while considering the context (i.e., job resources and job demands) and aspects of the implementation process. A longitudinal quantitative study was conducted that involved employees and managers in two hospitals and one municipality (n = 448). Applying the job demands-resources model, multiple linear regression models were used. VSM, standardised work and 5S promoted employees and managers’ working conditions when supported by job resources. When no support was provided, visual follow-up boards were inhibiting employees and managers’ job satisfaction. VSM and standardised work were seen as central lean tools. In this sample, the application of lean cannot be considered sustainable as employees and managers’ working conditions deteriorated under the implementation of lean.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".