Second-Year Baccalaureate Nursing Students’ Decision Making in the Clinical Setting
Bibliographic record
Abstract
ABSTRACT This qualitative, intrinsic case study explored the decision-making activities of baccalaureate nursing students in the second year of a 4-year program. It sought to discover how second-year students determine the need to make a clinical decision, how they respond to a pending clinical decision, the types of decisions made in the clinical setting, and the factors that enhance or impede decision making. The study involved 12 students, all of whom were enrolled in their first clinical rotation on an inpatient unit and completed journals and interviews. Inductive analysis revealed three key encounters that demonstrated students’ decision making: encounters with the patient, nursing staff, and clinical tutor. Each encounter revealed an emotion-based and knowledge-based response to various clinical situations. Decisions were evident within each of the three encounters. Implications for curriculum development and clinical tutors are described. AUTHORS Received: May 18, 2004 Accepted: January 20, 2005 Dr. Baxter is Assistant Professor, and Dr. Rideout is Associate Professor (retired), McMaster University, School of Nursing, Hamilton, Ontario, Canada. Address correspondence to Pamela Baxter, PhD, RN, Assistant Professor, McMaster University, School of Nursing, 1200 Main Street West, Hamilton, Ontario, Canada L8N 3Z5; e-mail: baxterp@mcmaster.ca.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".