Development of the Proposed Solutions to Implement SPICES Model Strategies in Iranian Undergraduate Nursing Curriculum
Bibliographic record
Abstract
Background: SPICES model is one of the most popular strategies to assess, review, and modify curriculums. The objective of the present study was to determine SPICES model implementation in undergraduate nursing curriculums in Iran, Canada, and Australia and suggest solutions for the Iranian undergraduate nursing curriculum.\nMethods: This comparative study was conducted in 2019 using the Brady Model that includes description, interpretation, juxtaposition, and comparison. Ten top universities from the United States, Australia, and Canada as well as Iran were selected according to purposeful sampling. The curriculums of these universities were examined considering six strategies of SPICES model (i.e. student-centered, problembased, integration, community-based, elective, and systematic).\nResults: According to the implementation procedure of this strategy in famous universities, there are solutions to implement six strategies of SPICES model to modify and review the Iranian nursing curriculum.\nConclusion: According to the successful experiences of top nursing schools in the implementation of SPICES model, modification in nursing curriculum is essential considering the needs of the society and facilities.\nKeywords: NURSING CURRICULUM, SPICES MODEL, IRAN
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".