Critical Thinking and Clinical Decision Making Among Registered Nurses in Clinical Practice: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Critical thinking is fundamental for registered nurses (RNs) when making clinical decisions, which impact patient outcomes. This review aimed to identify studies on critical thinking and clinical decision making among nurses in clinical practice and synthesize their findings based on the regional area, observed findings, and predictive factors, and to assess the measurement tools used. Methods: A comprehensive search of the PubMed, Web of Science, CINAHL, and SCOPUS databases up to December 2024 was conducted in accordance with the PRISMA guidelines. The Newcastle–Ottawa Scale was used to assess the quality of included studies. Studies with similarly themed components were grouped for narrative synthesis. A meta-analysis of random-effects model calculations was performed. Results: This review included forty studies (twenty-four on CT, twelve on CDM, four on both) from various WHO regions, revealing diverse findings on observed skills. Ten CT and four CDM measurement tools were identified. Many studies also explored individual and group-level predictive factors for these skills. Meta-analyses of four common tools (CCTDI, NCT4P, CDMNS, and NDMI) showed significant heterogeneity, with statistically significant pooled mean scores. Conclusions: The synthesis highlights the global research on nurses’ critical thinking and clinical decision making, including the exploration of various predictive factors. However, the significant heterogeneity in the findings from meta-analyses of commonly used measurement tools underscores a need for more standardized measurement and analytical approaches, such as multilevel modeling, to better account for the hierarchical nature of potential predictive factors (individual and group levels), which would allow for more reliable comparisons and stronger conclusions in this field.
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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 | 0.039 | 0.100 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".