Positioning Canadian Nurses as Leaders in Responding to the Mental Health Impacts of Climate Change: A Call to Action
Bibliographic record
Abstract
The concept of planetary health draws nurses’ attention to environmental disruptions, including climate change, that threaten the health of humans and our broader ecosystems. Among the many deleterious consequences of climate change are its adverse effects on mental health. These impacts have been identified in communities across Canada, with some groups disproportionately affected. As such, this topic ought to be integrated into undergraduate or pre-licensure curricula delivered to all nursing students, regardless of eventual practice setting. While there are potential barriers to realizing this curricular addition, there are existing educational materials that can be used to support this change. Moving forward, Canadian nursing bodies can play an instrumental role in supporting transformation through the development and ratification of relevant entry-to-practice competencies and by supporting the dissemination of evidence-aligned educational resources on climate change and mental health. Climate change offers an opportunity for Canadian nursing organizations, at the provincial and national levels, to provide leadership in responding to one of the defining health crises of our era.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.016 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 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".