A Comparison of the Psychiatric Nursing Master`s Curriculum in Iran and Canada
Bibliographic record
Abstract
Background: One of the most important tasks of a university is to assess weaknesses and build upon strengths. As the education and skill level of psychiatric nursing graduates in Iran is often unpredictable, we hope to improve the current curriculum by comparing it to a successful educational program. Objectives: This study compared the MSc psychiatric nursing curriculum in Iran with Canada. Methods: This is a descriptive comparative study conducted in 2018. The required information was collected from the Iran Health Ministry curriculum and Canadian universities offering psychiatric nursing graduate programs. The method used was a Beredy model that includes description, interpretation, neighborhood, and comparison Results: The University in Canada has been established earlier than Iran. The educational program at Brandon University is focused on community needs. It is possible to complete program on a part- or full-time basis. Some courses are optional.Admission requirements include practical psychiatric nursing care experience and a relevant degree. In Iran the requirements are limited to a bachelor’s degree in nursing and an entrance exam. The program primarily focuses on theory, and was only offered on a full–time basis. Conclusion: The Iran educational program has weaknesses. In order to improve the quality of education, it is suggested students volunteer to have psychiatric nursing care experience. The curriculum should include administration, education, and practice. A more flexible curriculum based on the needs of Iranian society should be offered. Keywords: Curriculum, Master, Mental Health Nursing, Comparative Study, Canada, Iran
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".