Systematic strategy in nursing curriculum in American, Canadian, Australian nursing and proposed way for applying it in Iranian nursing curriculum: A comparative study
Bibliographic record
Abstract
ABSTRACT Introduction: Recently, a systematic strategy has 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 systematic strategy have applied in the nursing curriculum in the US, Canada and Australia and proposed methods for applying it in Iranian nursing curriculum. Methods: This comparative study was done according to Beredy’s model: Description, interpretation, juxtaposition, and comparison. The analysis was done on the curriculum of nursing colleges in the above mentioned countries. The samples were totally 10 colleges of 3 countries: USA, Australia, and Canada selected by purposive sampling. An inclusion criterion was applied to the systematic strategy in B.A. of nursing curriculum. Data collection instrument was five stages for applying systematic strategy based on the checklist. Nursing curriculum in these countries was retrieved through their publications, books, the Internet, their web sites and electronic communication. The internal validity and external validity of the documents were reviewed. Data analysis was performed according to Bredey's model. Result: This strategy is helpful for selecting students, effective teaching and learning process and outcomes Conclusion: Considering the systematic strategy in the nursing curriculum 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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".