High Fidelity Simulation and the Development of Clinical Judgment in Senior Nursing Students: A Mixed Method Approach
Bibliographic record
Abstract
Clinical judgment is defined as an understanding and interpretation regarding patient’s needs, health problems or concerns (Tanner, 2006). There are four interrelated processes in Tanner’s model that consist of noticing, interpreting, responding, and reflecting (Tanner, 2006). Because clinical judgment is extremely complex and encompasses many ways of thinking and types of knowledge, it requires a flexible capability to identify significant features of indeterminate clinical circumstances. Mixed methods study was conducted to describe senior nursing students’ experience in using high-fidelity simulation to evaluate the development of clinical judgment skills. A convenience sampling of 30 senior nursing students who signed the consent, met the inclusion criteria, and attend the selected school of nursing in the fall of 2020 were used for this study. All participants answered questionnaires regarding the quantitative survey. Participants interviewed face-to-face and video call using Zoom meeting program and recorded using an audio recorder. Both the quantitative and qualitative findings identified that learning through high-fidelity simulation supports the improvement in the participants’ clinical judgment skills. All participants reported their perceptions and experiences from using high-fidelity simulation develop and support their clinical judgment skills from the beginning through the end of the simulation, especially improving prioritizing data and working as a team with providing effective communication.
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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 | 0.031 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".