Pathophysiology and pharmacology course grades and prediction of success in a graduate nurse practitioner program
Bibliographic record
Abstract
BACKGROUND: During a program review, faculty identified that nurse practitioner (NP) students who received a C grade in Advanced Pathophysiology (Patho) and Advanced Pharmacology (Pharm) appeared to perform poorly in the later NP management courses and on other program outcomes. PURPOSE: The research aimed to determine whether grades in graduate Patho and Pharm courses could predict performance in NP management courses, program progression and completion, and certification pass rates. METHODOLOGY: This research included deidentified student data from 2016 to 2018 across seven NP specialty tracks ( n = 4,575). Nonparametric and parametric tests were used in the analysis. RESULTS: A significant correlation ( p < .001) existed between Patho and Pharm grades. Lower grades in these two courses were significantly related to each other and to lower management course grades. Logistic regression showed that graduate pathophysiology grades significantly predicted certification examination performance, with lower grades associated with lower certification examination performance. Graduate pharmacology grades, pathophysiology grades, composite management course grades, and admission grade point average (GPA) significantly predicted final cumulative GPA, with lower grades associated with lower performance for all variables. CONCLUSIONS: Results of this research support the hypothesis that grades of C in Patho or Pharm courses significantly predict C performance in the management NP courses and lower certification success rates. IMPLICATIONS: The project model can be used in future research. Study findings can be helpful to NP faculty when considering curriculum decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".