Modification of Nursing Education for Upgrading Nurses' Participation: A Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: The product of the educational nursing programs in Iran is training nurses who less have professional apprehension and commitment for participating in professional decisions. Whereas nurses especially those in high academic levels are expected to more involve in professional issues. OBJECTIVE: The aim of this study was to explore Iranian nurse leaders' experiences of making educational nursing policy with emphasizes on enhancement of nurses' participation in professional decisions. METHODS: We used a qualitative design with thematic analysis approach for data gathering and data analysis. Using purposive sampling we selected 17 experienced nurses in education and making educational nursing policies. Data gathered by open deep semi-structured face to face interviews. We followed six steps of Braun and Clarke for data analysis. RESULTS: In order to enhance nurses' participation in professional decisions they need to be well educated and trained to participate in community and meet community needs. The three main themes that evolved from analysis included opportunities available for training undergraduate students, challenges for PhD nurses and general deficiencies in nursing education. The second theme includes three sub-themes; namely, the PhD curriculum, PhD nurses' attitudes and PhD nurses' performance. CONCLUSIONS: We need for revising and directing nursing education toward service learning, community based need programs such as diabetes and driving accidents and also totally application of present educational opportunities. The specialization of nursing and the establishment of specialized nursing associations, the emphasis on teaching the science of care and reinforcing the sense of appreciation of pioneers of nursing in Iran are among the directions offered in the present study.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".