Bibliographic record
Abstract
BACKGROUND: Late preterm infants in the Maternal Child Services Department at a Midwestern medical center were cared for in 3 separate nursing units. Standardization of care was a performance goal for the Department. PURPOSE: A quality improvement process was implemented that included planning, teaching, performance application, and evaluation of evidence-based practice guidelines for care of the late preterm infant. METHODS: A web-based teaching module was developed to introduce nursing care guidelines for late preterm infants to the nursing staff. RESULTS: Analysis of the pre-and posttest scores embedded in the educational video showed a statistically significant increase in the nurses' knowledge about potential complications of infants born between 34 and 36 weeks' gestation. IMPLICATIONS FOR PRACTICE: Quality improvement process increases nurses' knowledge about care of the late preterm infant and can lead to standardization of care. IMPLICATIONS FOR RESEARCH: Ongoing quality improvement monitoring is needed for sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".