Strategies Used in Canadian Nursing Programs to Prepare Students for NCLEX-RN® Licensure Exam
Bibliographic record
Abstract
Nursing educators need strategies for preparing students to be successful in the National Council Licensure Examination (NCLEX-RN®). Understanding the educational practices used is an important step in informing curricular decisions and helping regulatory agencies evaluate nursing programs’ efforts to prepare students for practice. This study described strategies used in Canadian nursing programs to prepare students for the NCLEX-RN®. A cross-sectional descriptive national survey was completed by the program’s director, chair, dean, or another faculty member involved in the program’s NCLEX-RN® preparatory strategies using the LimeSurvey platform. Most participating programs (n = 24; 85.7%) use one to three strategies to prepare students for the NCLEX-RN®. Strategies include the requirement to purchase a commercial product, the administration of computer-based exams, NCLEX-RN® preparation courses or workshops, and time dedicated to NCLEX-RN® preparation in one or more courses. There is variation among Canadian nursing programs in how students are prepared for the NCLEX-RN®. Some programs invest considerable effort in preparation activities, while others have limited ones.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".