Leading Complex Change in Healthcare: 10 Lessons Learned
Bibliographic record
Abstract
This article reports on the transfer of perinatal services at St. Joseph's Health Care, in London, Ontario, to London Health Sciences Centre (LHSC). The transfer of perinatal programs, services and people/providers to LHSC generates concern in key stakeholders with respect to a potential negative impact on the quality of care delivery, staff work life and morale, team performance, recruitment, retention and other performance indicators. Our main task was to establish "readiness and capacity for the change" in the years leading up to the actual transfer, with a strong focus on attending to the human side of the change, clinical and cultural alignment. We describe the external and internal challenges of the transfer and the approach that we took in building readiness, and end with 10 lessons learned and applied throughout the change process.
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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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".