Integrating climate change into nursing curricula and continuing education: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Climate change constitutes a major threat to human health. Nurses have an essential role to play in protecting populations from this threat, and to fulfil this role, they must be properly prepared. The purpose of this scoping review is to examine studies on the integration of climate change into the academic curriculum or continuing education of nurses so as to identify issues and opportunities related to this integration. METHODS AND ANALYSIS: . First, a search strategy using keywords and their combinations will be developed. This strategy will be applied in four bibliographic databases: MEDLINE (PubMed), CINAHL, Embase, Web of Science. Second, an initial selection of studies based on titles and abstracts will be carried out by two members of the research team using the software Covidence. They will conduct this selection process independently, with the aim of identifying relevant studies that meet the inclusion criteria for our scoping review. Third, the second stage in the selection process will be carried out by examining the full text of each article to determine which studies to include in the review. Finally, data on year of publication, authors, geographical area, article type, study objectives, methodology and key findings will be extracted from selected articles for analysis. A search of the grey literature will also be conducted to supplement the results of the bibliographic database search. The scoping review is currently ongoing. Identification of relevant literature began in the first quarter of 2022 and is expected to be completed in the first quarter of 2023. ETHICS AND DISSEMINATION: Ethical approval is not required for this review. The results of this study will be presented in workshops and conferences and be submitted for publication to a peer-reviewed journal.
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 | 0.141 | 0.095 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.066 | 0.020 |
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".