Admission academic metrics and later success in an accelerated master’s entry program
Bibliographic record
Abstract
Background: Accelerated master’s entry programs for non-nurse college graduates leading to advanced practice, which are both rigorous and fast-paced, utilize academic metrics to evaluate prospective candidates, including GRE scores and GPA levels. Because this program saw an increased rate of failure from the program (with medical-surgical nursing being associated with > 93% of failures), the aim of this study was to examine if either of these metrics were associated with later success in the program.Methods: A retrospective, descriptive study analyzed admission metrics and first year academic performance to determine if any criteria were associated with academic success. Data collected included age, gender, race, ethnicity, GPA, GREs and scores on the seven required courses in the first 25 weeks. T-tests, correlations, ANOVAs and multiple regression were used to determine if any significant relationships existed.Results: Admission data from 333 students revealed no differences in the mean GPA related to academic success. Student who failed out of the program had significantly lower GRE quantitative, verbal, and writing scores. Additionally, quantitative and verbal scores correlated with exam scores on many didactic courses, and explained 25.4% of the variance in the first medical-surgical exam scores (p < .001), with GRE quantitative scores having the most effect.Conclusions: This study demonstrated verbal and quantitative scores were the only predictor of academic success suggesting admission offices should reconsider whether this current trend of omitting GREs is meeting the needs of students, faculty, universities and the public at large.
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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 | 0.003 | 0.017 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".