Perceptions of Graduates From a Canadian Bachelor of Nursing Program: Preparing for the Registered Nurse National Council Licensure Examination (NCLEX-RN)
Bibliographic record
Abstract
As a self-regulated profession, nursing in Canada is based on legislation enacted by provincial and territorial associations with the purpose of protecting the public from harm (Marquis & Trajan, 2012). Since 1970, most Canadian jurisdictions required completion of national examinations to obtain professional licensure (Elliott, Rutty, & Villeneuve, 2013; Kovner & Spetz, 2013). In 2011 the Canadian Council of Registered Nurse Regulators (CCRNR) announced the Canadian Registered Nurse Examination (CRNE) would be replaced with the National Council Licensure Examination-Registered Nurse (NCLEX-RN) developed in the United States as the new requirement for registered nurses (RN) to enter practice in Canada. The implementation of the NCLEX-RN in 2015 stimulated extensive dialogue among nursing stakeholders in Canada. Preliminary exam results indicated that the first cohort of Canadian NCLEX-RN writers had lower scores than both previous CRNE results and the NCLEX-RN pass rates of writers in the United States (Hobbins & Bradley, 2013; PennellSebekos, 2015). A key factor impacting NCLEX-RN success is strategies used by candidates to prepare for the exam. This paper describes research undertaken to investigate the perceptions of the first cohort of graduates from an Atlantic Canada bachelor of nursing (BN) program about their NCLEX-RN preparations. The investigation focused on strategies employed by participants prior to, and after program completion, and how these preparations aligned with their experiences in writing the exam.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".