Environmental stewardship, sustainability, and planetary health related to infection prevention and control
Bibliographic record
Abstract
Infection prevention and control (IPAC) practices are both impacted by and contribute to climate change and pollution. The World Health Organization (WHO, 2018) has identified climate change as the greatest global threat of the 21st century. Canada is warming more than twice as fast as the global rate, and the Canadian Arctic almost four times as fast (Rantanen et al., 2022). Extreme weather events from climate change are increasingly impacting the health of Canadians directly, through heat stroke (Adam-Poupart et al., 2014, 2015) and cardiorespiratory issues (Paterson et al., 2012; Levison et al., 2018). Global warming has contributed to the rise in diseases like Lyme disease in Canada, spread by vectors which can now live further north (Ogden et al., 2014; Canadian Medical Association, 2022; Romanello et al., 2024), and the simultaneous emergence of multidrug-resistant Candida auris in three continents may be linked to thermal adaptation (Casadevall et al., 2019).
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".