NCLEX‐RN<sup>©</sup> preparation resources available online in French: An integrative review
Bibliographic record
Abstract
Abstract Aim This review describes the availability of online French NCLEX‐RN© preparation resources for candidates Background One entry to practice requirement for Canadian nurses is to successfully pass a licensing exam upon graduation from their educational program. In 2015, the American NCLEX‐RN© replaced the Canadian entry to practice licensing examination which was offered in Canada's two official languages: English and French. The NCLEX‐RN© was developed in English and later translated to French. Since its implementation, Francophone candidates and educators in Canada have reported a lack of preparatory resources available in their language and have had substantial lower NCLEX‐RN© pass rates, consistently below 50% Methods An integrative review using Whittemore and Knafl's framework was conducted between February and May 2019, and updated in September 2020, through online searches of CINAHL, PubMed, Science Direct and Google Scholar databases. Grey literature was included from 2012 onwards. Results are presented narratively. Results A total of 17 French language preparatory resources were found. These resources were categorised into four main groups: (1) What is the NCLEX‐RN©?; (2) What do I need to do prior to writing the NCLEX‐RN©?; (3) What is assessed through the NCLEX‐RN©? and finally, (4) How can I practice before taking the NCLEX‐RN©? Conclusion Limited French‐language NCLEX‐RN© preparatory resources exist for Francophone candidates. Furthermore, practice questions in French are few compared to what is available in English Implications for Nursing and Nursing Policy Other countries may consider implementing an entry to practice exam such as the NCLEX‐RN© because of its availability in both the French and English language, as well as the possibility of translating the exam to other languages, creating a potential market for this test around the globe. The lack of preparatory resources in French is a major concern to Francophone candidates undertaking such a high‐stakes examination in their language. Nursing stakeholders and policy leaders should acknowledge that such gaps place Francophone writers in a disadvantaged position in comparison to their Anglophone counterparts.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".