Nursing students blood pressure measurement accuracy during clinical practice
Bibliographic record
Abstract
Background: Blood pressure measurement is a complex skill to master and we sought to determine whether nursing students could measure blood pressure accurately on patients during their first clinical placement. We also examined whether clinical facilitator’s subjective rating of nursing student’s competence and confidence was related to blood pressure measurement. Methods: First year nursing students (n = 105) blood pressure measurement was determined at the end of 40 hours of clinical placement. Clinical facilitators (n = 17) assessed blood pressure accuracy of the students using a double-headed stethoscope on clinical patients and rated the student’s confidence and competence levels in blood pressure measurement across the clinical practicum. Results: Bland Altman plots revealed that there was no systematic bias and that that majority of student’s blood pressure readings were within ±4 mmHg of the clinical facilitators. Blood pressure measurement was not significantly different between students and clinical facilitators (systolic: p = .29; diastolic: p = .96). Statistically significant correlations between clinical facilitator’s ratings of student confidence, competence and blood pressure accuracy were found. Conclusions: These findings show that blood pressure accuracy in nursing students during their first clinical placement is high. Clinical facilitators can also correctly assess student’s blood pressure accuracy using subjective ratings of competence and confidence, which may be sufficient to determine clinical proficiency.
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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 | 0.008 | 0.055 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".