Internationally educated nurses’ competency assessment and registration outcomes
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine relationships between internationally educated nurses’ (IENs’) performance in a registered nurse competency assessment process and the outcomes of their nursing registration applications. Assessments of nursing practice competencies, IEN applicant characteristics and registration outcomes were explored. Design/methodology/approach This is a secondary statistical analysis of a subset of IEN application data from a previous study in combination with assessment data from an additional database. Application data between 2008 and 2011 were analyzed using univariate/bivariate analyses and regression models to explore the relationship of performance in the assessment process and outcomes of the registration process. Findings Competency categories IEN applicants had difficulties with (from least to most) were Professional Responsibility and Accountability, Ethical Practice, Self-Regulation, Service to the Public, Knowledge-Based Practice: Specialized Body of Knowledge and Knowledge-Based Practice: Competent Application of Knowledge. IENs educated in the UK and USA had the highest scores and odds of meeting competencies. Applicants educated in India and Asia had lower scores and odds ratios. All national entry-to-practice examination and registration eligibility competencies were significantly related to registration outcomes. Applicants passing the exam had higher competency scores while applicants ineligible for registration had lower competency scores. Research limitations/implications Limitations include integrity of data extracted from active databases, IEN motivation to complete the RN registration process and conversion of assessment scales for research analysis. Originality/value Results inform regulation policies that improve IEN registration processes and may be informative to regulators, assessment centers, educational institutions and IENs.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".