The H-PEPSS: an instrument to measure health professionals' perceptions of patient safety competence at entry into practice
Bibliographic record
Abstract
BACKGROUND: Enhancing competency in patient safety at entry to practice requires introduction and integration of patient safety into health professional education. As efforts to include patient safety in health professional education increase, it is important to capture new health professionals' perspectives of their own patient safety competence at entry to practice. Existing instruments to measure patient safety knowledge, skills and attitudes have been developed largely to examine the impact of specific patient safety curricular initiatives and the psychometric analyses of the instruments used thus far have been exploratory in nature. METHODS: Confirmatory factor analytic approaches are used to extensively test the Health Professional Education in Patient Safety Survey (H-PEPSS), a newly designed survey rooted in a patient safety competency framework and designed to measure health professionals' self-reported patient safety competence around the time of entry to practice. The H-PEPSS focuses primarily on the socio-cultural aspects of patient safety including culture, teamwork, communication, managing risk and understanding human factors. RESULTS: Results support a parsimonious six-factor measurement model of health professionals' perceptions of patient safety competency. These results support the validity of a reduced version of the H-PEPSS and suggest it can be appropriately used at or near training completion with a variety of health professional groups. CONCLUSIONS: Given increased demands for patient safety competency among health professionals at entry to practice and slow, but emerging changes in health professional education, ongoing research to understand the extent of patient safety competency among health professionals around the time of entry to practice will be important.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".