Clinical Reasoning on an Assignment: Baccalaureate Nursing Students' Perceptions
Bibliographic record
Abstract
Baccalaureate nursing students must develop strong clinical reasoning skills to make sound clinical judgments regarding patient care. The purpose of this Interpretive Descriptive qualitative study was to explore how students understand the evolution and application of their own clinical reasoning skills. Eight nursing students were interviewed about their perceptions regarding the use of clinical reasoning skills on a written, patient scenario based assignment. An overarching theme of Over Time emerged along with two themes: Understanding of Clinical Reasoning and Making Sense of the Assignment. Sub-themes were identified as not knowing, knowing, applying knowing and valuing knowing. Students understood their clinical reasoning skills to have progressed throughout their educational program; perceived that their understanding of the patient’s problem and the required nursing actions deepened over the time of writing the assignment; perceived they were able to apply learning from the assignment to their nursing practice; and perceived writing the assignment to be a stressful experience. Implications for nursing education include leveling of assignments; completing the assignment in pairs or small groups to improve learning and decrease stress; and the incorporation of virtual patient or high-fidelity simulation to improve the visual and unfolding elements of a patient scenario based clinical reasoning assignment.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".