Self-reported patient safety competence among new graduates in medicine, nursing and pharmacy
Bibliographic record
Abstract
BACKGROUND: As efforts to address patient safety (PS) in health professional (HP) education increase, it is important to understand new HPs' perspectives on their own PS competence at entry to practice. This study examines the self-reported PS competence of newly registered nurses, pharmacists and physicians. METHODS: A cross-sectional survey of 4496 new graduates in medicine (1779), nursing (2196) and pharmacy (521) using the HP Education in PS Survey (H-PEPSS). The H-PEPSS measures HPs' self-reported PS competence on six socio-cultural dimensions of PS, including culture, teamwork, communication, managing risk, responding to risk and understanding human factors. The H-PEPSS asks about confidence in PS learning in classroom and clinical settings. RESULTS: All HP groups reported feeling more confident in the dimension of PS learning related to effective communication with patients and other providers. Greater confidence in PS learning was reported for learning experiences in the clinical setting compared with the class setting with one exception-nurses' confidence in learning about working in teams with other HPs deteriorated as they moved from thinking about learning in the classroom setting to thinking about learning in the clinical setting. CONCLUSIONS: Large-scale efforts are required to more deeply and consistently embed PS learning into HP education. However, efforts to embed PS learning in HP education seem to be hampered by deficiencies that persist in the culture of the clinical training environments in which we educate and acculturate new HPs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".