Transforming Healthcare Organizations Looking Back to See the Future
Bibliographic record
Abstract
he preceding papers in this issue of Healthcare Quarterly provide a "how-to" guide to mounting a complex, across-the-organization change, and also reveal the unique perspectives of the different professional groups involved in the change. In addition, the paper "Executive Perspective: The Business Case for Patient Safety" (see p. 20 in this issue) reveals how the University Health Network's (UHN) Executive Team came to the decision to pursue the specific Medication Order Entry/Medication Administration Record (MOE/MAR) initiative. Each paper in this issue of HQ ended with "Lessons Learned" unique to each UHN leader's perspective. In contrast, this paper looks back on the five-year initiative, from all perspectives, in order to provide a final set of observations for organizations considering the implementation of a MOE/MAR-type project. More generally, this paper speaks to healthcare leaders who are contemplating significant changes in their organizations.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".