A Comparative Study of Nursing PhD Curriculum in Iran and Bloomberg University of Canada
Bibliographic record
Abstract
Introduction: Nursing PhD program provides essential competency for professional role playing among the graduated according to society’s needs, but there are differences in the provided programs the analyzing of which would develop the discipline. This study aimed at comparing the curricula of nursing PhD programs in Bloomberg University and Iran. Materials and Methods: This descriptive-comparative study was carried out based on Bereday’s model in 2018 and compared the components of nursing PhD program curriculum in Iran and Bloomberg University. The data were collected by searching through the internet and described, interpreted, juxtaposed and compared. Results: Although there were similarities, the purpose, the curriculum and the program implementation were different in these two universities. Different contents of the curriculum, educational methods, and the expected role of graduates were considerably different in Bloomberg University. Conclusion: In order to improve the quality of PhD nursing graduates in Iran, it is suggested that the content and method of providing courses be reviewed according to the roles and expectations of graduates and society’s needs. For exploring the weaknesses and strength of the program and providing more information for future planning, it is suggested that the quality of the curriculum be evaluated from the viewpoint of PhD students and faculty members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".