Reframing Resource Stewardship and Sustainability as Professionalism: What Can Efforts for a Net-Zero Health System Learn from Choosing Wisely campaigns?
Bibliographic record
Abstract
Miller and Xie (2020) issue a compelling and wide-ranging call to action for how healthcare systems, leaders and decision makers can and should mobilize to address the climate crisis. Issues of sustainability and the climate crisis are complex, wicked problems with no simple solutions. Sustainability considerations and the imperative to use healthcare system resources wisely are a motivator of the Choosing Wisely Canada campaign. These considerations are increasingly urgent in the context of fiscal and resource deficits due to the COVID-19 pandemic. The experience of Choosing Wisely campaigns can offer potential strategies for driving increased awareness and action from healthcare stakeholders on the climate crisis. This commentary explores some of the factors that have contributed to the spread of Choosing Wisely campaigns and the levers that have fostered campaign uptake. It will also consider the challenges faced by, and lessons learned, from Choosing Wisely campaigns, and how these could inform healthcare systems and individual clinicians in increasing awareness and taking leadership on the climate crisis.
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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.056 | 0.052 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".