Nursing curriculum in some developed countries and proposed way of applying it in the Iranian nursing curriculum A comparative study
Bibliographic record
Abstract
Introduction: Recently, student- centers have used for improving quality of nursing curriculum that traditional curriculum is not suitable for it. The aim of the present study is to identify how the student- center strategy have applied in nursing curriculum in America, Canada and Australia and proposed methods for applying it in the Iranian nursing curriculum. Methods: This comparative study was conducted according to Beredy’s model: Description, interpretation, juxtaposition, and comparison. The population was the documentation of world nursing colleges. The samples were totally 10 colleges of 3 countries: USA, Australia, and Canada who were selected by purposive sampling. The inclusion criteria were applying student- center in B.A. nursing curriculum. The data collection instrument was 5 stages of student center strategy which developed by Changiz & et al (2006). Nursing curriculum in these countries, were retrieved through their publications, books, the internet, their Sites and communicating electronically. The validity of these documents was assessed by internal validity and critique the external validity were reviewed. Data analysis was performed according to Beredy’s model. Results: Based on applying this strategy, proposed methods for applying student- center strategy are to inform students about rules, events and student growth- students research center- using new and various teaching and evaluation methods, advisory center- revised curriculum based on students’ view- attention to educational outcomes. Conclusion: There are different ways for applying student-center in nursing curriculum that Attention to them can promote Iranian nursing curriculum.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".