Supporting Canadian Nursing Students to Write the NCLEX-RN Exam: A Three-Phased Mixed Methods Descriptive Design
Bibliographic record
Abstract
BACKGROUND: In 2015, the College of Nurses of Ontario, replaced the Canadian Registered Nurse Examination with the NCLEX-RN exam as entry-to-practice. Faculty in a college-university partnership searched for products to provide nursing students with focused practice in writing exams modelled on the Canadian NCLEX-RN test plan. PURPOSE: The aim of this three-phased evaluation study was to test and validate NCLEX-RN exam preparation materials newly developed for the Canadian context. METHODS: A mixed methods descriptive design was used to capture subjective perspectives and objective measures. After ethical approval was obtained, 13 students assessed the e-learning platform's usability. Eight faculty/clinical experts assessed the content validity of materials using a content validity index (CVI) at both item (I-CVI), and scale (S-CVI) levels. Lastly, 72 completed tests served as the basis for assessing psychometric properties of selected test items. RESULTS: Materials were assessed as useful and easy to use and navigate. I-CVIs ranged between 0.5 to 1.0 with none falling below 0.5 while S-CVIs were above the standard for acceptability of greater than 0.8 with none falling below 0.9. Overall test reliability measured by the Kuder-Richardson formula was 0.73. Many items assessed for difficulty (64%) showed a proportion of correct responses within desired ranges, and most point-biserial indices ranged from fair to very good. CONCLUSION: Strong evidence supported the usability and content validity of the materials assessed. Item difficulty and discrimination analyses were within acceptable ranges. Suggestions for improvements were offered. Predictive analysis should form the basis of future research in this area.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".