Nursing MSc Theses: A Study of an Iranian College of Nursing and Midwifery in Two Decades (1990-2010)
Bibliographic record
Abstract
AIM: A thesis is an important part of nursing graduate students' education, which is also their first systematic and scientific attempt to learn the ABCs of research. Articles derived from theses are important for the dissemination of science and the improvement of nursing as a field. Therefore, it is the goal of the present research is to analyze the different aspects of nursing MSc theses and the number of published articles derived from them. METHODS: This was a descriptive research carried out on 145 nursing MSc theses defended in Razi Faculty of Nursing and Midwifery in Kerman between 1990 and 2010. All of the extracted data were put into an Excel file (2007 version) followed by a data analysis. RESULTS: The results of this study were then presented via the use of descriptive statistics and figures. The research findings showed that most of the theses used a descriptive or analytical-descriptive method, and 42% of them had patients as their participants. They were usually delivered on the subject of health care, and only 58 articles were extracted from the whole 145 theses. CONCLUSION: The process of writing nursing MSc theses and thesis research articles is improving gradually. However, there is a growing need for empirical and semi-empirical research to bridge the gap between theory and practice, which is also a major concern among nurses.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".