Climate Change Integration in Nursing Academic Curricula and Continuing Education: A Scoping Review
Bibliographic record
Abstract
Background and PurposeThe consequences of climate change on individuals' and communities' health are numerous. Nurses are among the healthcare professionals most confronted with the climate crisis, and have great potential to limit its impact on vulnerable populations. However, our knowledge of educational options for preparing nurses to address climate change remains limited. The aim of this scoping review was to examine how climate change is integrated into the academic curricula or continuing education of nurses.Methods and ProceduresArksey and O'Malley's (2005) framework was used to conduct this scoping review. Documentary search strategies were developed and tested in four databases, and a search of the grey literature was carried out. A two-stage selection process was implemented. Data from 16 scientific articles were extracted and 11 grey literature references were included in the analyses. A narrative summary allowed to synthesize the findings.ResultsAcademic and healthcare organizations are increasingly implementing innovative and participatory educational initiatives to raise nurses' awareness of climate change's health impacts and encourage students and nurses to act in their daily lives and clinical practice. Challenges include a dense curriculum, the absence of a climate competency framework, and theoretical content being presented in an unstimulating manner.ConclusionsThe use of innovative, interactive teaching methods, reference to a theoretical model centered on planetary health, and climate change content distributed throughout the curriculum are some ways to stimulate students' interest in climate issues. Collaborative efforts involving academia and organizations are needed to foster nurses' awareness and encourage a variety of climate-oriented actions with planetary reach.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".