Bibliographic record
Abstract
Optimization in the health sector is a hot topic and there are many expert opinions on how to proceed. Based on research to be conducted with selected health care facilities will be identified approaches that have a basis in the experiences of successful applications in Scandinavia, the USA, Canada Portugal and many world countries. Another outcome will be providing comprehensive basic material conditions and methods of implementation options based on the Toyota Production System summarized the philosophy of Lean Healthcare in healthcare facilities. To find optimal solution I aplicate methods of Kaizen Lean methodology also related to Total Service Management and Total Flow Management of KAIZEN Institute, where I cooperate on projects, and enrich by the other partial findings and results from my planned research for internal business needs and looked at this issue in terms of current trends and new perspectives and make provision for this methodology from experience of Western countries. The main methods used I have analysis that comes from audits and comparison current states of observed organizations and hospital facilities and outcoming systhesis where are recommendations determined and shown real results and benefits. The main point is to show what is the principle, what problems are associated with implementations, and what benefits they bring. And based on this work to make more accessible to organizations that view optimization and show that this way is the right one.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| 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".