Considerations for subspecialty preparation for nurse practitioners
Bibliographic record
Abstract
The number of nurse practitioners (NP) entering pediatric gastroenterology is increasing. Most nurse practitioners acquire knowledge and skills through special NP fellowship programs and on-the-job training. However, professional competencies have yet to be defined or standardized. The authors sought to evaluate subspecialty nursing organizations' role in developing education programs that improve NP preparation for practice. Nurse Practitioners completed an assessment survey to determine the need for an education program and the type of education program desired. Based on the survey feedback, the authors created a pediatric gastroenterology-focused education program through the National Association of Pediatric Nurse Practitioners (NAPNAP) in partnership with the Association of Pediatric Gastroenterology and Nutrition Nurses (APGNN). A pre-test/post-test design was utilized to determine knowledge obtainment. Post-test knowledge scores supported the program's ability to increase preparation. A post-test score increase was noted among new NPs and those who were members of specialty organizations. The development, implementation, and evaluation of standardized competencies and education programs through specialty organizations should be considered.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".