Bibliographic record
Abstract
In this paper we’ll discuss how change management affects hemodialysis improvement. As hemodialysis is a technology dependent method of End Stage Renal Disease (ESRD) treatment, it is obvious that the need of continuous revisions in health care practices and researches on staff training are significant factors for success. “Change” defined as an attempt to replace existing knowledge with new. Change achievement is not always a simple procedure. In this study we examine nurses and patients reactions on changes and how can we accomplish successful changes every time they are needed. We also examine how changes in role of health care team can lead the team to our final destination, which is to provide the best hemodialysis treatment we can. Leadership, communication, informing, planning, and adjusting are the main contents for successful change management. We believe that we can improve haemodialysis practices and health care by giving learning opportunities to our nurses. Nursing training development can help them to follow changes. On the other hand we can get our patients to come on board with us on any change by encouragement and consistent try. As Hereticus said, “Nothing that is, has to be just because it is.” Therefore, we should keep thinking about managing changes all the time!
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".