Aplicación de Lean Manufacturing en el sector sanitario.: Un análisis bibliométrico
Bibliographic record
Abstract
The present research performs a bibliometric exercise of the literature on Lean Manufacturing applied in the health sector. For this purpose, the systematic review method proposed by Denyer and Tranfield (2009) is used, which consists of identifying and formulating the research question, locating the literature, selection criteria and evaluation of the studies, analyzing and synthesizing the information, presentation and reporting of the research results in a study timeline of no more than 10 years. Databases such as PubMed for scientific articles and Google Scholar for books were considered. This database was used because the research is a pioneer in the study of the phenomenon and therefore, free access to information through both databases guaranteed access, development and analysis of the scientific production. The results of the bibliometric analysis established a total of 298 articles in PubMed and 46 books in Google Scholar, analyzed according to three criteria: number of articles and books published per year, country of origin and type of document. A growth trend was shown in research on Lean Manufacturing located in the United States, Canada and the United Kingdom. On the other hand, the research revealed that Lean manufacturing is not limited only to the industrial sector, as research in the health area was identified.
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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 | 0.036 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.149 | 0.172 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".