Use of NCLEX Preparation Strategies in a Hospital Orientation Program for Graduate Nurses
Bibliographic record
Abstract
This article describes outcomes from the first year of a hospital orientation program for graduate nurses that was expanded to systematize and enrich preparation of graduate nurses for success on the NCLEX-RN licensure examination. The study protocol provided the Assessment Technologies Institute predictor examination to assess risk for licensure examination failure, review materials, and a meeting with an education specialist to identify and prioritize study needs. Those at highest risk for failure were also provided an in-depth written study plan and ongoing follow-up and support until the licensure examination was taken. The study sample consisted of 90 graduate nurses who were hired from May through August of 2005 at the University of Kansas Hospital. The pass rate for participants was 86.7% on the first attempt in year 1 of the program. At-risk graduates who reported that the predictor results impacted their study habits and followed the study recommendations were more likely to pass the licensure examination. Graduate nurses reported a high level of satisfaction with the support provided. Specific challenges faced by hospital nurse administrators in recruitment and retention and return on investment over a 3-year improvement plan are described.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".