Facilitating critical thinking and clinical judgment in clinical nursing education
Bibliographic record
Abstract
Nurses who can critically think and make clinical judgments in the clinical setting are crucial to safe and effective nursing care. This type of critical thinking and clinical judgment is best developed during clinical education, which provides students with the opportunity to bridge the theory to practice gap. Clinical instructors guide students’ development of critical thinking and clinical judgment in the clinical setting. Despite clinical instructors having such a significant impact on critical thinking and clinical judgment there is limited research on how they facilitate students’ development of critical thinking and clinical judgment. This qualitative analysis explored how clinical instructors facilitate the development of critical thinking and clinical judgment of nursing students. This study used interpretive description to analyze interviews with eight clinical instructors to develop themes and subthemes within the data. These themes were discussed in relation to definitions of critical thinking and clinical judgment, indicators for evaluation, clinical teaching strategies, and contextual facilitators and barriers. Clinical instructors who took part in this study defined the concept of critical thinking as : The process that leads to clinical judgment, the decision. Evaluation was based on broad indicators of patient safety, effective communication, students’ confidence in their ability to critically think and make clinical decisions, and taking ownership of their patient care. Clinical instructors identified a variety of clinical teaching methods that they adapted and individualized to specific student needs. Clinical instructors also use multiple strategies to meet student needs, which they then adapt for each cohort. Contextual factors also impact students’ development of critical thinking and clinical judgment such as buddy nurses, the school of nursing curriculum, clinical ]instructors, and the nursing student themselves. This study identified suggestions for curriculum development, clinical instructor development, and potential areas for future research in relation to clinical education. Clinical instructors are key components of nursing education and the development of critical thinking and clinical judgment in nursing students; as such, it is important to understand their perspectives on how they develop these student abilities.
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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 | 0.030 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".