A 15-Year Trend Study of Internationally Educated Nurses’ NCLEX-RN Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it