BLOOD PRESSURE MEASUREMENT METHODS AND KNOWLEDGE, PERCEPTION, PRACTICE OF NURSES: ARE WE THERE YET?
Bibliographic record
Abstract
Objective: Accurate Blood pressure measurement (BPM) is a fundamental aspect of hypertension management. Guidelines regarding BPM methods, namely home (HBPM), ambulatory (ABPM), office (OBPM) and automated (AOBP) are published by Hypertension Canada. Nurses commonly perform BPM, but little is known regarding their knowledge, perception and practice when considering all BPM methods. A recent scoping review concluded that insufficient data was available for all professionals, but specifically nurses. This descriptive study assessed knowledge, perception and practice of nurses regarding all BPM methods. Design and method: All nurses licensed to practice in primary care settings across Québec were targeted for this survey. A personalized email invitation, and 2 reminders, including a link to a secured platform was send between in December 2019. Data was collected using a validated and pretested questionnaire consisting of 48 questions for knowledge, perception and practice for all BPM methods in English and French. Results: A total of 447 nurses participated, with a response rate of 11%. The median age was 40.5 ± 11,5 years old and 92% were women. For knowledge and perception, the mean percentage score obtained are presented in Table 1. For practice, the most frequently recommended method was HBPM (47%), followed by ABPM (18%). One third (35%) nurses reported education for HBPM was frequently provided to their patients, while for ABPM it was only 18%. While AOBP is the preferred method in Canada, more than half (52%) never use it and only 57% use oscillometric device for OBPM, meaning that there are still a large number of measurements being performed manually by auscultation. Conclusions: Although nurses had positive perceptions about the use of out-of-office measurements there is still much room for improvement for knowledge and practice. Our results show that a gap remains between what is recommended, what is known, and what is actually done in the field. Further strategies should involve optimal use of educational resources such as BPM algorithm posters and booklets, optimizing patient centered care using team-based approach, incorporating HBPM and encouraging participation in certification programme offered by hypertension societies.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".