A 15-Year Trend Study of Internationally Educated Nurses’ NCLEX-RN Performance
Bibliographic record
Abstract
AIM: The aim of the study was to describe trends in internationally educated nurses' (IEN) National Council Licensure Examination-Registered Nurses (NCLEX®-RN) performance from 2003 to 2017 and to determine the odds of passing the exam based on country of nursing education. BACKGROUND: IEN comprise 5.6 percent of US nurses; more than half come from the Philippines. There is a lack of research on IEN NCLEX-RN performance. METHOD: Correlational research was used to determine the performance and likelihood of passing the NCLEX-RN based on country of nursing education using secondary data analysis. Odds ratios were estimated to express the odds of passing. RESULTS: IEN NCLEX-RN applications and pass rates are decreasing. The odds of passing the NCLEX-RN among Philippine-educated nurses are lower compared to all other IEN. The odds of passing the Canadian NCLEX-RN are higher for all IEN. CONCLUSION: The low NCLEX-RN pass rate of IEN reflects differences in nursing education and practice across countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".