Improving the Quality Management System of the Biomedical Engineering Department at Niagara Health as per Best Practices Recommended by Canadian Medical and Biological Engineering Society
Bibliographic record
Abstract
The Biomedical Engineering Department at Niagara Health has gone through a peer review conducted by the Canadian Medical and Biological Engineering Society (CMBES) in September 2015. The peer review is a voluntary assessment where the quality of services provided by the Biomedical Engineering Department at Niagara Health is compared with the most current Clinical Engineering Standards of Practice. Before this review, a quality management system was implemented by the department. Upon review of the existing system in place, CMBES came up with 29 recommendations to improve on, for the next peer review scheduled for fall 2018. This article describes the strategies deployed to consolidate and improve the existing system by incorporating the recommendations of CMBES. The current system was amended, and new processes were developed to better serve the purpose, vision, values, and success factors of Niagara Health, as well as the 4 focus areas laid out in the 10-year strategic plan of the organization.
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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 | 0.086 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".