Bibliographic record
Abstract
he journey to this issue on the National Council Licensure Examination (NCLEX) began when the CJNL editorial director received two unsolicited NCLEX-focused manuscripts for consideration to publish.Adding to the growing body of articles and news stories published about the NCLEX since its 2015 implementation in Canada, the two papers revealed that the NCLEX-RN remains a controversial and unresolved issue for our profession.CJNL has long published special issues to focus attention on specific topics.Such focused publication creates a convenient repository of knowledge and information about a given subject, facilitating deeper exploration of the issue and serving as a historical reference point for the topic going forward.This issue continues that tradition by presenting 10 papers that discuss the NCLEX from the perspectives of researchers, policy makers, educators, students and a coalition of nurses that is vigorously advocating for remedies to address the disproportionately adverse impact of the NCLEX on students and especially on francophone writers.As CJNL's editor for policy and innovation, I was asked to serve as guest editor for this issue, and it has been my privilege to do so.A registered nurse for more than four decades, I have an abiding interest in the structure, power and politics of our profession, including the principle of self-regulation granted to nursing and other health professions through legislation and regulation in Canada.By way of self-disclosure, I was the Canadian Nurses Association's (CNA) president-elect from 2010 to 2012, during which time the Canadian Council of Registered Nurse Regulators (CCRNR) was formed and subsequently (in 2011) announced the change to the NCLEX from the Canadian Registered Nurse Examination, which had been owned and administered by CNA as the entry-to-practice examination for the profession.It is important to note that in the course of preparing this issue, outreach was made to CCRNR by CJNL's editor-in-chief (Dr.Lynn Nagle) on two occasions: first as the issue was being planned and again when the last of the 10 papers had been received.On both occasions, CCRNR was invited to contribute its perspectives.A response was received to the second communication stating that CCRNR was unable to contribute but appreciated the offer.The e-mail also asked if the articles could be shared prior to publication.As it is not customary to share articles in press, the request was declined.
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.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.080 | 0.020 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 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".