Bibliographic record
Abstract
For the past three years, I have written this column on green healthcare, but this will be my last one. My work now has moved on – or back, to be more precise – to focus on public health services, away from green healthcare. In this final column, I want to reflect on the progress we have made and the challenges ahead. When we began the Canadian Coalition for Green Health Care in 2000, the topic was very peripheral to healthcare providers. It came in part out of a concern by some environmental organizations (notably Great Lakes United, the Toronto Environmental Alliance and the Canadian Centre for Pollution Prevention) with the environmental impact of the healthcare system, and partly out of concern on the part of several health professional organizations (notably the Canadian Association of Physicians for the Environment, the Canadian College of Health Service Executives, the CMA and the CNA) and a few hospitals (notably Toronto’s Hospital for Sick Children and Cambridge Memorial Hospital) with both the practical benefits of “greening” the healthcare system and with the ethical duty of healthcare professionals to do no harm to people, and by extension to the environment within which people live. This, in turn, came in part from a concern with the health of the environment itself and the many species that live within it, and in part from a recognition that doing harm to the environment results in doing harm to people. At the outset, although a group of (mainly) national organizations helped establish the coalition, much of its work became focused in Ontario. In particular, the Ontario Hospital Association became a willing and enthusiastic partner (and more recently, a member) and this led to three of the key activities of the coalition at the OHA annual convention: The Green Lane in the exhibits area featuring environmentally responsible products and services, an annual educational presentation featuring high profile speakers (Bob Rae this year, Ontario’s Environment Commissioner before that) and the Green Health Care Awards, which recognize outstanding achievements in Ontario in the areas of energy efficiency, pollution prevention, individual leadership and overall leadership. In addition to this set of annual events, the coalition has maintained a website (www.greenhealthcare.ca) and developed some key resources – notably a 70-page resource on many different aspects of “greening” healthcare (Doing Less Harm) that was funded by the Office on Sustainable Development at Health Canada – and a checklist for building green hospitals. The membership has slowly expanded to include a number of hospitals (several of which have attained ISO 14001 certification) and has some members beyond Ontario, notably in Winnipeg, Edmonton and Vancouver. A firmer base has thus been established for the next, necessary phase of the development of the coalition and its expansion to become a truly national organization that will play a key role in the greening of the Canadian healthcare system and the attainment of its vision, which is that “Canada’s healthcare system operates in an environmentally responsible manner.” If this bold vision is to be achieved, there are a number of challenges that need to be addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.030 | 0.031 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.029 | 0.042 |
| Insufficient payload (model declined to judge) | 0.051 | 0.022 |
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".