Creating an evidence-based pediatric urology advanced practice provider orientation program
Bibliographic record
Abstract
The authors sought to understand the background and training of pediatric urology advanced practice providers (APP) to create an orientation program that improves APP preparation for subspecialization in pediatric urology. Obtaining these data will allow for the development of an evidence-based approach to educating the new pediatric urology APP at a national level. An anonymous survey was sent to the 331 members of the Pediatric Urology Nurses & Specialists (PUNS) professional organization to assess the qualities of orientation and training at institutions across the nation. Descriptive statistics were used to characterize the results of the survey for this group of participants. A total of 49 participants (15% response rate) participated in this survey. Most participants reported completion of a one to three-month orientation program (57.2%) that was moderately structured (57.1%). The overall mean self-rating of preparedness was 6.2 out of 10 at the conclusion of their training. However, this rating varied between participants who had training programs under and over three months duration (p < .001). Most respondents felt that they were not optimally prepared to enter the field. The survey results show that longer training programs (at least three months) lead to higher levels of self-assuredness. The pediatric urology APP orientation program at the authors' institution will be adjusted to meet the concerns of the survey participants to ensure a more equipped team of advanced practice providers. The authors hope that this will serve as a guideline for institutions across the country to train pediatric urology APPs.
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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 | 0.034 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".