Exploring the business of urology: Change management
Bibliographic record
Abstract
Change is inevitable. All organizations need to change to maintain relevance and to successfully adapt to varying external forces. Change is a challenge for those involved in it. It is the antithesis of consistency and at its core requires a shift in behaviour that is reflexic and instinctual. The authors of this paper would suggest that given the nature of healthcare, its evolving research, its social value/importance, and its cost, that no other profession is more subject to change than medicine. Change is necessary, inevitable, and difficult. A solid understanding of change and its management process is a prerequisite for professional survival. Preparation, conviction, purpose, and clarity are the core values of change. Physicians, more than any other profession, have been driven to and exposed to change. Physicians are in a position to distill its process to a level of understanding that would serve as a model for others to follow. The goals of this paper are to outline the rationale of change, as well as to review change management options and strategies to increase successful change.
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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".