The educational system and curriculum of Ph.D nursing students in Iran and Toronto, Canada: a comparative study
Bibliographic record
Abstract
Introduction: Today, the main concern of health education system is neglecting the quality of materials and the efficiency of students. Matching, comparing and analyzing different dimensions of programs with those of other countries would be helpful in solving this problem. This study was conducted with the aim of comparing the Ph.D. Nursing Program in Iran and Toronto. Methods: This descriptive study was carried out using a comparative approach and Beredy model. The relevant information was collected from two universities and classified according to the interpretation and adjacent stages. Then, the similarities and differences were analyzed. Results: Both programs are full-time, in-person, and emphatically student centered. There are structural differences between the programs; but there are similarities during the educational period and the content of some courses, such as research education, research proposals and critique studies in Toronto, with the methodology of quantitative-qualitative research and critique of papers in Iran. Students' Proposal writing course faces similar challenges. Toronto does not focus on the article extracted from the thesis, but in Iran, it is considred necessary. Conclusion: Toronto's curriculum has been updated. In Iran, considering the increasing needs of graduates and the expectations of graduates, is necessary to make general changes. Therefore, it is recommended that in addition to the suggestions given in various texts to modify the program, the course in Iran should be reviewed in general terms and content. Citation: Roshanzadeh M, Tajabadi A, Aghaei M. The educational system and curriculum of Ph.D nursing students in Iran and Toronto, Canada: a comparative study. Journal of Development Strategies in Medical Education 2018; 5(2): 40-62.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".