Clinical Nursing Reasoning in Nursing Practice: A Cognitive Learning Model Based on a Think Aloud Methodology
Bibliographic record
Abstract
Background. The current context of increasingly complex nursing care requires a high level of clinical reasoning in nursing practice. Still, teaching clinical reasoning in nursing remains a challenge for educators in the field. Although several studies have been conducted to try to understand clinical reasoning in nursing, neither its developmental stages nor the corresponding critical milestones have been uncovered. Therefore, nursing educators cannot rely on a cognitive learning model (a description of how people learn and develop a specific competency) to facilitate the learning of this crucial competency. Objectives. This study was conducted to develop a cognitive learning model of clinical reasoning in nursing, from the beginning of education to the development of expertise, highlighting the critical milestones corresponding to each stage. Design. A descriptive design based on the think aloud method was used. Settings and participants. The study was held in one university and two associated hospitals. Individual interviews were conducted with 45 undergraduate nursing students and 21 registered nurses (RNs). Participants were asked to think aloud, using five clinical scenarios that were validated in a previous study. Analysis. Interviews were transcribed verbatim and subsequently analyzed using the protocol analysis method recommended for think aloud studies. Results. Five developmental stages and their related critical milestones were identified and used in the elaboration of a cognitive learning model for clinical reasoning in nursing: (1) internalization of the idea that nursing is a scientific profession; (2) learning to read and use scientific literature in care planning and nursing interventions; (3) learning to move from data collection to hypothesis generation to nursing interventions; (4) integrating wards’ routines and protocols; (5a) towards professional expertise or (5b) towards task-oriented practice. Conclusion. Recommendations for teaching and learning clinical reasoning in nursing during initial and continuing education in nursing are suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".