Decision support strategies for bedside nursing clinical reasoning: A scoping review
Bibliographic record
Abstract
Background: Proficiency in clinical reasoning is crucial for achieving positive patient outcomes in healthcare. Nurses should take an active role in fostering their clinical reasoning skills. Bedside support strategies aim to offer practical, invaluable, and easily applicable resources for restructuring cognitive processes in situations of complex clinical demands. Objective: This review aimed to map evidence on bedside decision strategies applied by nurses to support clinical reasoning. Methods: The review followed the Joanna Briggs Institute recommendations for scoping reviews. Information: Sources Studies were retrieved from Medline (PubMed), CINAHL (EBSCOhost), Web of Science Core Collection, Scopus, Cochrane CENTRAL, RCAAP (Repositórios Científicos de Acesso Aberto de Portugal), and Google Scholar. Studies published in Portuguese, English, Spanish, or Swedish, without imposing geographical or cultural restrictions were considered. Qualitative and quantitative studies on bedside decision strategies that support nursing clinical reasoning were included, while studies exclusively focused on education, case-specific content, or Artificial Intelligence applications were excluded. Results: Out of 1889 results, 14 studies met the inclusion criteria. The studies were conducted in countries such as China, New Zealand, Norway, Brazil, Indonesia, Canada, Ireland, the United Kingdom, United States, and Australia, involving nurses, patients, and healthcare teams, with study designs ranging from qualitative research and cohort studies to quasi-experimental and theoretical analyses.These studies identified two key strategies: normative (following guidelines) and reflective (based on critical self-reflection). Conclusions: Reflective strategies are widely used at the bedside, emphasizing the importance of adaptability in uncertain clinical environments. Despite Artificial Intelligence's role in clinical decision-making, fostering critical thinking and decision-making skills in nurses remains essential. Registration: OSF https://doi.org/10.17605/OSF.IO/H96VQ. Tweetable abstract: A scoping review identified normative and reflective bedside strategies used by nurses to support clinical reasoning, highlighting the value of adaptability and critical thinking in complex care settings. #ClinicalReasoning #Nursing #PatientCare.
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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 | 0.025 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".