Comparative comparison of Nursing PhD curriculum in Iran and Toronto, Canada
Bibliographic record
Abstract
Introduction: The rapid expansion of nursing education in recent decades has raised concerns about the quality of education, competency of graduates and nursing professional growth. Comparative studies are one of the strategies to improve the quality and validation of educational systems. This study endevored to compare the PhD curriculum in nursing in Iran and the School of Nursing, University of Toronto, Canada. Methods: This descriptive-comparative study was done in the academic year 2019 through an overview of the literature, components of nursing doctoral curricula in Iran and Toronto universities by seeking the relevant online resources with Persian and English keywords "PhD in Nursing", "Nursing Education" and "curriculum". Beredy model was used for comparative comparison. Results: The PhD curriculum in Nursing at the Universities of Iran and the University of Toronto has focused on the professional values, promoting community health, social justice, innovation and student-centeredness; though, in the Iranian Nursing PhD curriculum, the focus has been on Islamic values and scientific development, and in Toronto, with a transnational perspective, emphasis has been placed on cultural values and the ability of international leadership. In the Iranian curriculum, unlike Toronto, most of the content of the courses is theoretical in nature, and there is no clear relationship between the courses and the health needs of the community and students' needs and abilities. Contrary to the strict policy of publishing an article from a doctoral dissertation in Iran, this is not the case in University of Toronto. Conclusion: Findings revealed that the doctoral curriculum in nursing in Iran has not addressed the health needs of society effectively, and lack of transnational perspective has reduced the ability of graduates to lead and participate in global health and international research. These conditions make the necessity of reviewing educational programs and contents as well as amending the relevant laws inevitable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".