Achieving Environmental Excellence through a Multidisciplinary Grassroots Movement
Bibliographic record
Abstract
St. Joseph's Healthcare Hamilton (SJHH) supports a grassroots green team, called Environmental Vision and Action (EVA). Since the creation of EVA, a healthy balance between corporate projects led by corporate leaders and grassroots initiatives led by informal leaders has resulted in many successful environmental initiatives. Over a relatively short period of time, environmental successes at SJHH have included waste diversion programs, energy efficiency and reduction initiatives, alternative commuting programs, green purchasing practices, clinical and pharmacy greening and increased staff engagement and awareness. Knowledge of social movements theory helped EVA leaders to understand the internal processes of a grassroots movement and helped to guide it. Social movements theory may also have broader applicability in health care by understanding the passionate engagement that people bring to a common cause and how to evolve sources of opposition into engines for positive change. After early successes, as the limitations of a grassroots movement began to surface, the EVA team revived the concept of evolving the grassroots green program into a corporate program for environmental stewardship. It is hard to quantify the importance of allowing our staff, physicians, volunteers and patients to engage in changes that they feel passionately about. However, at SJHH, the transformation of a group of people unsatisfied with the organization's environmental performance into an 'engine for change' has led to a rapid improvement in environmental stewardship at SJHH that is now regarded as a success.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".