The lived experience of new graduate nurses successfully completing NCLEX-RN
Bibliographic record
Abstract
Background and objective: The NCLEX pass rate is considered the primary indicator of program quality. Much literature exists regarding pre-graduation efforts aimed at aiding students to prepare for the NCLEX-RN exam, while there is little available on post-graduation efforts. This project was conducted to identify the post-graduation experiences of successful NCLEX-RN test takers as they prepared to complete the exam.Methods: This was a qualitative descriptive study utilizing a phenomenological framework to determine the lived experience of new graduates preparing to complete the NCLEX-RN exam.Results: Four main themes were identified as relevant to post graduation experiences including: (a) Finding Motivation, (b) Study Tactics, (c) Taking a Break, and (d) The Testing Experience. Additionally, few of the participants took it for granted that they were going to pass the exam, they reported wishing they had spent more time preparing, and with regard to studying, several described wishing they had started earlier.Conclusions: It will be beneficial for faculty to discuss potential strategies for success to utilize after graduation, including expectations of testing day, setting a realistic timetable to test, overcoming lack of motivation to preparation for the exam, and careful scheduling of coaching and study sessions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".