Obtaining an accurate maternal blood pressure: A quality improvement initiative to improve nursing knowledge and confidence
Bibliographic record
Abstract
Background: Inaccurate assessment of maternal blood pressure (BP) contributes to misdiagnosis of hypertension, unnecessary or missed interventions, and maternal morbidity. This study examines obstetric nursing knowledge and confidence in proper assessment of maternal BP before and after an institutional quality improvement project.Methods: We implemented an online educational initiative in our women’s health unit based on the American Heart Association’s Blood Pressure Improvement Program. Simultaneously, a standard assessment of BP cuff sizing by arm measurement was implemented. We conducted a pre- and post-intervention assessment of nursing knowledge and confidence of BP measurement. Responses were analyzed using the χ2 test, two-sample t test, ordinary least squares and logistic regression.Results: A total of 145 nurses completed the pre- and 68 completed the post-intervention assessments. Participants answered 62% of pre- and 73% of post-intervention questions correctly (p < .001). Before implementation, 86.9% of participants reported feeling very or extremely confident in obtaining an accurate BP measurement, increasing to 98.5% following (p = .007). 73.8% of pre-intervention respondents reported feeling very or extremely confident in choosing an appropriate BP cuff compared to 96.3% post (p < .001). Following implementation, confidence levels were similar irrespective of years in practice, years of experience at our hospital, and primary nursing unit.Conclusions: A BP educational initiative and standardized BP cuff assessment increased nurses’ knowledge and confidence in selecting the correct cuff size and obtaining accurate readings. Increased knowledge and confidence may lead to greater adherence to standardized BP assessment during peripartum admission, more accurate BP measurements, and improved management of hypertensive disorders in pregnancy.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".