Bibliographic record
Abstract
The study explores the key factors influencing Lean management and evaluates their individual weights to identify what leads to a successful hospital management. It adopts the 4P Excellence Model in Lean management to assess and measure the healthcare organizations from the five perspectives: leadership, people, partnership, processes, and products. To explore the potential factors, the study employs the analytic hierarchy process (AHP) method and multi-expert judgment to prioritize the significance of each factor. The study has led to a number of useful insights. Processes are crucial when hospitals advocate lean management. Among the 18 sub-factors in the five criteria, the most significant factors include patient-centered care, clearly defined work content, rewarding teamwork effort, continual learning and upgrading, and increasing the clinic quality. To satisfactorily carry out Lean management, a hospital should continuously strive for improvement, pursue perfection, engender organizational culture, strengthen teamwork, and create mutual trust among team members. Moreover, patient-centered care beliefs should be actively implemented. To provide seamless care, patients and their families should be the main foci. The results could be used by hospital managers to improve their skills and knowledge when implementing Lean management. In addition, the framework developed herein could potentially lend itself to many practical applications.
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".