Bringing the Fight Against Climate Change to the University: Determinants, Strategies, Mechanisms, and Desired Outcomes
Bibliographic record
Abstract
Despite calls for nurses to be trained, there are few data on strategies used to incorporate climate change issues into university curricula. Approach: A narrative literature review of 45 articles addressing educational practices related to the fight against climate change was conducted. The Implementation Research Logic Model guided the analysis of 1) aspects of the articles’ respective contexts that are favorable or unfavorable determinants; 2) incorporation strategies that have been deployed; 3) action mechanisms that have been used; and 4) expected outcomes. Results: Three strategies were identified: exemplification, integration, and inclusion. The literature identifies various themes, learning objectives, and teaching methods associated with these strategies, though few articles explore their impact on learning. However, contextual determinants can still be linked with chosen mechanisms and strategies to select the approach that best fits the context and can overcome potential obstacles. Recommendations are made to account for how students feel about environmental issues, the potential impact of this, practical changes that may be needed, and the role of research in this process. Conclusion: The initiatives on record are flexible and offer ways to think about how to incorporate climate change issues into health care curricula while allowing for both the complexity of these issues and their academic, professional, and social significance.
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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 | 0.033 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".