Evaluating intentional quality rounding for undergraduate student nurse training during COVID-19
Bibliographic record
Abstract
Nursing’s body of knowledge is ever expanding, incorporating new theoretical constructs such as quality and safety and care transitions we now consider central to the domain of nursing, and to nursing clinical education. The purpose of this article is to describe an educational quality improvement project, an alternative clinical learning experience during COVID-19 that enabled the implementation and evaluation of Bachelor of Science in Nursing (BSN) students in an intentional quality rounding process. We designed and implemented a retrospective, observational quality improvement educational project. Programmatic evaluation was used to obtain feedback from 273 pre-licensure students using a 10-item Likert scale evaluation tool in June 2020. Students averaged a 4.33 rating on the evaluation of the intentional quality rounding clinical experience as something they should incorporate into future nursing practice. A critical role for nursing education is the development of innovative teaching strategies and learning experiences that facilitate the student in the translation and application of complex constructs from nursing’s expanding body of knowledge, a task made more difficult by the COVID-19 pandemic.
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".