Bibliographic record
Abstract
magine you are a member of a hospital's executive team, having just left a meeting in which you and other members discussed the possible introduction of an ambitious Computerized Physician Order Entry (CPOE) system. Around the conference table you and others questioned whether CPOE would be the most effective way to realize your hospital's commitment to patient safety. Other issues that were raised included whether clinicians would support or resist the change, whether staff would have sufficient skills, where to begin, affordability and whether to proceed incrementally or with a "big bang." While there was much disagreement with respect to each of the issues, there was near unanimity around two important decisions -CPOE would be implemented and you would be the executive responsible for the system's design and implementation. This article, based on the experiences of a multi-site hospital, and drawing on past research on organizational change, provides a Four-Stage model to help change leaders in healthcare. Although relying on Toronto's University Health Network to illustrate the change model, the model is intended to speak to change leaders implementing various types of complex changes in all healthcare organizations.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".