Nursing Students’ Views of Nursing Education Quality: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Nursing education is currently facing challenges related to the application of nursing knowledge in clinical environments and inability of students in application of nursing procedures in clinical settings. Nursing students themselves represent the best means of identifying these challenges. This study was conducted aimed to understand the nursing students' viewpoints and experiences concerning the challenges and deficiencies of the nursing education system. METHODS: This qualitative study that has been carried out adopting conventional qualitative content analysis approach, 40 senior nursing students with sufficient experience of educational situations participated through purposive sampling. Eight focus group discussions were done with volunteer nursing students from School of Nursing and Midwifery in Zahedan (Iran). All of the interviews and discussions were recorded and then analyzed using the conventional content analysis approach. RESULTS: Three themes were emerged from data analysis including theoretical education, clinical skills, and the gap between theoretical education and clinical skills. CONCLUSIONS: The students' views and experiences of nursing education quality (theoretical, clinical, and the gap between theoretical education and clinical skills) demonstrated a need to pay careful attention to the selection and recruitment of clinical teachers, and the assessment and control of their educational performance and clinical skills, as well as to determination of standards and validation of education quality.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".