Healthy Hospitals: A Journey to ISO 14001 Certification
Bibliographic record
Abstract
Studyith many health researchers now focusing on determinants of health we have seen a wide range of initiatives that go beyond "traditional" healthcare -things such as breakfast programs for children, literacy programs, and after-school activities.Inspired by our hospital's vision of being "an excellent community hospital that contributes to making our community as healthy as possible," we were ready to take on a non-traditional area of healthcare -the environment.The first steps in achieving our vision started directly inside the organization; we knew we had to get our own house in order.Thus, through the development of a comprehensive Environmental Management System (EMS), Cambridge Memorial Hospital (CMH) did just that and, as a byproduct, became the first hospital in North America to be certified as ISO 14001 compliant.Approximately 100 km west of Toronto, CMH is the only hospital in Cambridge and has a reputation for delivering high-quality, patientfocused care to the residents of Cambridge and North Dumfries.The 277-bed hospital serves a community of approximately 120,000 people, with services including acute, ambulatory and long-term care.The hospital underwent a significant re-engineering process in 1995/96, resulting in an exceptionally flat organizational structure based on a program management model.Over recent years the Board of Directors led an important shift for the hospital in its vision, moving from a model of "curing the sick" to one focused on improving the health of the community.This vision embraced improving health and wellness of the local environment.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".