Nurses’ attitudes and behaviors during bachelor of nursing students’ clinical learning experiences
Bibliographic record
Abstract
Objective: This study aimed to examine the nurse-student relationship during clinical learning experiences.Methods: Students at all levels of a Bachelors nursing program completed the Nursing Student Perception of Civil and Uncivil Behaviors tool (NSPCUB) after clinical experiences during each semester over one calendar year at a small Midwestern university. The tool included 12 items, four demographic questions, and two qualitative questions.Results: A total of 302 surveys were returned. The majority of surveys were completed by second semester students on a medical-surgical unit. The majority of students had positive experiences. Night shift nurses had a significantly higher mean on two variables. There was also statistical significance between second and third semester students on two variables. There were no statistical differences between units and hospitals. Student’s comments were mostly positive, though negative experiences still occurred.Conclusions: Nurses can positively impact student’s clinical learning experiences. Students have both positive and negative experiences in the clinical setting. Several positive themes were identified including role modeling, skill acquisition/teaching, communication and critical thinking development. Negative themes also occurred including rudeness, feeling ignored and inappropriate behavior. Further research is recommended.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".