Assessment of an educational intervention to improve nurses’ knowledge of blood pressure measurement
Bibliographic record
Abstract
Background: Accurate blood pressure (BP) measurement is a constant challenge, particularly in intensive care units. Thus, studies focusing on avoiding errors in practices of measuring BP are important to patient safety. The objective was to assess the effects of an educational intervention addressing BP measurement, targeting the theoretical and practical knowledge of nurses from a cardiac unit.Methods: This quasi-experimental, before-and-after, study was conducted in a large tertiary hospital in Brazil and included all nurses working in that unit (31 nurses, 86.1%). Data were collected through two types of assessments: practical and theoretical knowledge of the technique, before-and-after the educational intervention that involved simulation as a teaching strategy. A validated checklist was used for both assessments.Results: Most participants were female (64.5%), with an average age of 33.1 years old. Considerable improvement was observed in theoretical and practical knowledge concerning the steps used for BP measurement (p < .05). Considering the total sample, nurses complied with all steps of the BP measurement after educational intervention and the results were considered statistically significant (p < .05).Conclusions: The educational intervention improved the knowledge of nurses, which may contribute to safer healthcare delivery and error-free BP measurements.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".