Iranian nursing students' experiences of nursing.
Bibliographic record
Abstract
BACKGROUND: The negative attitudes and behaviors of Iranian nursing students impede learning and threaten their progression and retention in nursing programs. The need to understand students' perception and experiences of nursing provide knowledge about effectiveness of nursing education program as well as their professional identity. The purpose of this study was to discover experiences of nursing students. MATERIALS AND METHODS: In a descriptive, exploratory and qualitative study, twelve senior nursing students of Isfahan University of Medical Sciences (School of Nursing and Midwifery) were participated. Data was collected via unstructured in-depth interview, and thematic analysis method was used for analyzing the data. FINDINGS: The findings from this study revealed that the nursing students in Iran experienced altered experiences during their education program as positive and negative. Two major themes were constructed from the thematic analysis of the transcripts: professional dimensions and professional conflicts. CONCLUSIONS: Regarding the findings, positive experiences of students have leaded them to acceptance and satisfaction of nursing and negative experiences to rejection and hating of nursing and lack of adaptation with their professional roles. Therefore, it is recommended that revision and improvement in nursing education program is essential to facilitate positive experiences and remove negative experiences of nursing student's educational environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".