Academic success: Which factors contribute signify- cantly to NCLEX-RN success for ASDN students?
Bibliographic record
Abstract
Background: Accelerated Second Degree Nursing (ASDN) programs have become a vital means of addressing the nursing shortage resulting in a sharp increase in the number of these programs. In 2009, there were 230 accelerated programs and 33 in formal planning stages. Yet there has been little research to understand the predictive value of key components of these nursing programs for academic achievement as demonstrated by NCLEX-RN success. The purpose of this study was to determine the most significant factors and predictive value of these factors as they relate to student success on NCLEX-RN in an ASDN program. Methods: A retrospective predictive correlational design was employed to examine relationships between performance in pre-nursing and nursing courses as well as standardized tests for 363 ASDN students and NCLEX-RN success. A significance level of 0.05 was maintained for the analyses in this study. Results: The research results showed a high correlation with the first Medical/Surgical course as well as the pre-nursing course, Pathophysiology and offers new insight that is important in early identification of ASDN students who may or may not be successful on NCLEX-RN. More importantly, it sheds light on the factors, such as specific nursing courses, that best prepare ASDN students for RN-NCLEX success. Conclusion: The research results offer new insight that is important in early identification of ASDN students who may not be successful on NCLEX-RN. More importantly, it sheds light on the factors, such as specific nursing courses, that best prepare ASDN students for NCLEX-RN success.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".