Abstract P-052: SMOOTH TRANSITION FOR HEALTHCARE PROVIDERS MOVING FROM AN OLD PICU TO A NEW UNIT
Bibliographic record
Abstract
Aims & Objectives: Smooth transition for health care providers moving from an old PICU to a new one Methods Our strategies included the utilization of Early Implementation Opportunities and a comprehensive Education Training and Orientation (ETO) program. New innovative roles for Clinical Leadership and Implementation specialists were deployed. The clinical lead role provided the PICU with program representation from a clinical lens for design build, equipment procurement, workflow analysis/development, move planning and training. Implementation Specialists, working collaboratively across departments and with ETO leadership, designed, planned and delivered a 2 day curriculum that comprehensively trained and oriented the PICU’s interprofessional staff. Results The interprofessional team and support services of the PICU were successfully prepared to safely and efficiently provide care in the new Teck Acute Care Center from Day One of operations. Over 640 education sessions were prepared and delivered to this team of almost 200 employees. Over 95% compliance with completion of sessions occurred to provide a high level of preparedness prior to move in. Conclusions Ongoing daily evaluations during the first two weeks following move in provided positive insights to the level of confidence and preparation reported by the front line staff. Daily huddles also provided ongoing illumination of any issues requiring attention and resolution, captured through a structured “flows of medicine” as outlined in lean methodology.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".