Exploring the impact of critical thinking on community health nursing education in Canada: a scoping review
Bibliographic record
Abstract
• What is currently known about this topic? Critical thinking is essential in community health nursing education in Canada. • What the paper adds to the existing knowledge? This review identifies key contextual and educational factors influencing CT skills. • How the information in your manuscript can be applied to practice? Findings support improved CHN teaching methods to enhance students' critical thinking. To synthesizes the existing evidence about the contextual and educational factors that influence the development of critical thinking in undergraduate nursing students in community health nursing placements in Canada. This study employed a scoping review literature. A comprehensive search was conducted in CINAHL (EBSCO), Medline (Ovid), Embase, and ERIC databases. Peer-reviewed studies published between 2010 and 2024 were included. Two independent reviewers performed the screening process, data extraction, and quality assessment using Covidence, following the Arksey and O'Malley framework and PRISMA guidelines. Out of the 435 Canadian studies imported for screening, ten met the inclusion criteria. These studies highlight the significance of educational methodologies, such as virtual simulation, alongside contextual factors like the impact of the COVID-19 pandemic, which are crucial in shaping the development of critical thinking. Enhancing critical thinking skills in nursing students benefits their future practice by preparing them to address the complexities of community health nursing in Canada effectively.
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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 | 0.040 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.032 | 0.049 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".