Experiences of Undergraduate Nursing Faculty Adapting to the Changes of the Next Generation NCLEX
Bibliographic record
Abstract
Undergraduate nursing faculty are responsible for preparing nursing students for patient care in a dynamic health care setting and are challenged with meeting the National Council Licensure Examination (NCLEX) change to the Next Generation NCLEX (NGN). The National Council of State Boards of Nursing recommended the incorporation of the Clinical Judgment Measurement Model (CJMM) and competency-based curriculum in preparation for NGN testing that began in 2023. Understanding how faculty experienced and adapted to changes in curriculum, patient-centered instruction, and NGN testing may help future nurse faculty in adapting. The purpose of this basic exploratory, qualitative study guided by the achievement goal theory was to explore the experiences of nursing faculty in adapting to the change of the NGN and the incorporation of the CJMM in nursing curriculum and testing. Twelve undergraduate nursing faculty from the United States and Canada were recruited through social media to participate in online interviews until saturation of data was achieved. Audio recorded data from the open-ended interview questions were analyzed using Saldana’s first and second cycle coding and final phase of thematic review method. Analysis revealed three themes to describe the experiences of faculty: (a) adapt to change, (b) hindrance to adapt, and (c) resources that are needed to adapt. A quantitative study exploring the changes made while adapting to the CJMM and student readiness to practice is recommended for future research. Positive social change with improved nursing education and student readiness to provide patient care may be possible as programs of undergraduate nursing education provide guidance, access to resources, collaboration, and mentorship to support faculty.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".