Knowledge, perception and practice of Québec nurses for ambulatory and clinic blood pressure measurement methods: are we there yet?
Bibliographic record
Abstract
BACKGROUND: Guidelines regarding blood pressure measurement (BPM) methods, namely home (HBPM), ambulatory (ABPM), office (OBPM) and automated (AOBP) are published by Hypertension Canada and rely on accurate measurement technique. Nurses commonly perform BPM but their knowledge, perception and practice considering all methods is understudied. This study is the first to establish the picture of Québec nurses working in primary care settings concerning the four BPM methods. METHODS: All nurses licensed to practice in primary care in Québec were targeted in our survey. Data were collected using a validated and pretested investigator-initiated questionnaire in English and French. A personalized e-mail invitation, and two reminders, including a link to a secured platform was sent in December 2019. A certificate of ethics was issued by UQTR. RESULTS: A total of 453 nurses participated in the study. Median age was 40 ± 11 years, and 92% were women. The overall score on BPM methods knowledge was slightly below 50% (46% ± 23). The perception was mostly positive, with an overall score above 50% (73% ± 8). In practice, HBPM was recommended by 47% of nurses, and ABPM by 18%. Although AOBP is the preferred method in Canada, only 25% of the nurses use it, including the 57% that use an oscillometric device and 11% that use manual auscultation. CONCLUSION: Nurses working in primary care play a central role in BPM. Our results highlight that overall knowledge and practice are suboptimal. Resources should, therefore, be allocated to ensure that initial training and continuing education are addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".