Role of Performance Measurement in a Major Redevelopment Project: The Case of the McGill University Health Centre Transition Support Office
Bibliographic record
Abstract
Healthcare is currently in the midst of a construction boom. An increasing number of hospitals are being constructed using the principles of evidence-based design to improve the quality and safety of patient care while at the same maximizing efficiency. As the McGill University Health Centre embarks on a redevelopment journey, performance measurement has been deemed to be a key requirement for monitoring progress toward established objectives. This article discusses the role played by performance measurement in supporting the redevelopment project. Specifically, the importance given to performance measurement, the need for a performance evaluation framework, a description of the framework and the measurement process are presented.
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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 | 0.174 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.036 | 0.031 |
| Scholarly communication | 0.028 | 0.012 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".