Innovations in health institutions of the federation of Bosnia and Herzegovina (FBH), innovation management, innovation management models and support method
Bibliographic record
Abstract
Innovation is the process of transforming the idea into its practical application. Innovation and invention are two different concepts. The criterion by which we distinguish innovation and inventiveness refers to the connection between the practical and the commercial aspect. In essence, innovation consists of a theoretical concept, technical invention and commercial exploitation. An implicit feature of innovation is that it must be useful. This distinguishes innovation from the invention, which has no practical application. This revised work deals with numerous secondary sources of information, such as the Internet, books, as well as statistical reports on innovation and the countries of the EU, America, Canada, and OECD countries; He studied a number of professional articles, manuals, master theses, doctoral dissertations, etc. Satisfied patient is the goal of all our innovative healthcare activities. The main topic of the discussion is whether innovation is a process or result? Innovation management applies to all types and forms of innovation and innovative processes. In conclusion, a constant change for the better, which improves the health and satisfaction of patients, reduces health care costs, is an imperative for every serious health institution. Pluralistic approaches and ratings show that the quality of health services has a different meaning for patients - clients, health workers and managers. It is possible to innovate if we fully understand the nature of the challenges we face and if we manage to mobilize human resources for the necessary changes.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".