Patient safety education and perceptions of safety culture in American and Canadian urological residency training programs.
Bibliographic record
Abstract
INTRODUCTION To assess the perception of patient safety culture and the infrastructure to support patient safety (PS) education within American and Canadian urological residency programs. MATERIALS AND METHODS: A needs assessment was developed by experts in patient safety. The survey contained items about prior PS education, perceived value of learning PS, components of an ideal PS curriculum, and desired resources to facilitate PS education. Select items from the validated AHRQ Survey on Patient Safety Culture (SOPS) were also included. The survey was distributed electronically (12/2018-2/2019) to all urology residents (RES) and program directors (PD) of urological residency programs via the Society of Academic Urologists. All responses were anonymous. RESULTS: A total of 26 PD (18.3%; 26/142) and 100 RES (6.7%; 100/1,491) completed the survey. Nearly all RES received PS training (79%), but this was lower for PD (42%). The majority of RES and PD felt that PS was an important educational competency (RES = 83%; PD = 89%) and a pathway for academic success (RES 74%; PD 84%). Both groups desired an online PS curriculum (RES = 69%; PD = 68%) with error causation models (RES = 42%; PD = 52%) as the primary topic to cover. Assessment of safety culture confirmed safety is a priority, but only 1 PD (5%; 1/19) and 25 RES (25%; 25/100) rated their residency program's overall safety grade as 'excellent'. CONCLUSIONS: PS education remains a priority for program directors and urological trainees. Both groups called for additional resources from urological professional societies for this education. To that end, an online, centralized, freely accessible PS curriculum is under development.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".