Always on duty - Fostering climate resilience in the nursing profession: A discussion paper
Bibliographic record
Abstract
BACKGROUND & PURPOSE: As with the SARS-CoV-2 pandemic, climate change is a global phenomenon reshaping the nursing profession. While nursing organizations have produced numerous position statements on nursing and climate change, these tend to focus exclusively on the profession's important role in mitigating and adapting health systems and providing climate-informed patient care. However, to adequately prepare for the acceleration of climate change impacts, we also need to focus on supporting the health and wellbeing of the nursing workforce. The purpose of this discussion paper is to examine key areas of climate vulnerability for nursing and provide recommendations that address these factors. DISCUSSION: We consider three factors that may negatively impact on nurses' health and well-being in relation to climate change. First, there are social locations at the individual and population level, in particular gender, as the majority of nurses are women, and age, as the global workforce is aging. Both of these social locations are well documented areas of climate vulnerability. Second, the aging infrastructure of healthcare facilities puts nurses at risk by exposing them to harmful environments, such as extreme heat and poor air quality. Third, there are consequences for nurses' mental health as a result of providing care during climate-related weather emergencies and growing awareness of the impacts of climate change. RECOMMENDATIONS: In response to these risk factors, we recommend urgent actions that will support and promote nurses' health and well-being. For example, workplace policies and environments should be adjusted to address the unique healthcare issues of an aging workforce that is primarily women. As well, actions that promote climate-resilient healthcare systems are needed. These actions include updating physical infrastructures as well as ensuring adequate staffing during climate-related weather emergencies. There is also a pressing need for interventions that provide mental health supports and psychological safety in the workplace for nurses.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".