Addressing gaps in quality and safety education during pre-licensure clinical rotations
Bibliographic record
Abstract
United States national reports have called for improvement in healthcare professions education to better address patient care outcomes. In response, an initiative titled “Quality and Safety Education for Nurses (QSEN)” has been adopted by nursing programs across the nation, which describes the six main competencies to be included in nursing curricula. As early adopters of the QSEN competencies, the University of San Francisco nursing faculty promptly threaded the material throughout the 4-year Bachelor’s of Science in Nursing (BSN) curriculum. Confident that the topics were well covered in the classroom, we then sought to learn how often our students practiced these skills during their assigned clinical rotations. After completing an IRB-approved observational study of junior-level BSN students, we tallied the actual number of minutes spent in each competency area while assigned to the patient care unit. Using a time-on-task author developed QSEN-based tool, we found that our students spent little to no time engaged in quality improvement, evidence based practice, or informatics. This is a very important finding, as it indicates that our students may not be sufficiently developing these particular skills during assigned clinical hours. Weaving the six QSEN competencies throughout the curriculum is a good start, but as we saw in our observational study, all of the competencies are not equally demonstrated in the clinical setting. Continuing to provide QSEN enriched didactic courses, adding targeted simulation experiences, and nourishing academic/practice partnerships may help bridge the gaps.
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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 | 0.023 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".