Clinical reasoning and clinical judgment in nursing research: A bibliometric analysis
Bibliographic record
Abstract
AIMS: To characterize the thematic foci, structure, and evolution of nursing research on clinical reasoning and judgment. DESIGN: Bibliometric analysis. METHODS: We used a bibliometric method to analyze 1528 articles. DATA SOURCE: We searched the Scopus bibliographic database on January 7, 2024. RESULTS: Through a keyword co-occurrence analysis, we found the most frequent keywords to be clinical judgment, clinical reasoning, nursing education, simulation, nursing, clinical decision-making, nursing students, nursing assessment, critical thinking, nursing diagnosis, patient safety, nurses, nursing process, clinical competence, and risk assessment. The focal themes, structure, and evolution of nursing research on clinical reasoning and judgment were revealed by keyword mapping, clustering, and time-tracking. CONCLUSION: By assessing key nursing research areas, we extend the current discourse on clinical reasoning and clinical judgment for researchers, educators, and practitioners. Critical challenges must still be met by nursing professionals with regard to their use of clinical reasoning and judgment within their clinical practice. Further knowledge and comprehension of the clinical reasoning process and the development of clinical judgment must be successfully translated from research to nursing education and practice. IMPLICATIONS FOR THE PROFESSION: This study highlights the nursing knowledge gaps with regard to nurses' use of clinical reasoning and judgment and encourages nursing educators and professionals to focus on developing nurses' clinical reasoning and judgment with regard to their patients' safety. IMPACT: In addressing nurses' use of clinical reasoning and judgment, and with regard to patient safety in particular, this study found that, in certain clinical settings, the use of clinical reasoning and judgment remains a challenge for nursing professionals. This study should thus have an effect on nursing academics' research choices, on nursing educators' teaching practices, and on nurses' clinical practices. REPORTING METHOD: Relevant EQUATOR guidelines have been adhered to by employing recognized bibliometric reporting methods.
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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 | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical 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 | 0.069 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.279 | 0.317 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".