Primary care nurses closing the education gaps for hypertensive older adults: An integrative literature review
Bibliographic record
Abstract
The high prevalence of hypertension among the Canadian older adult population is a significant public health care issue. The evidence indicates that primary care nurses are crucial in providing high-quality care and education for older adults diagnosed with hypertension. Further, nurses can close education gaps by focusing on lifestyle modification and action strategies. This project aimed to conduct an integrative literature review investigating the proposed research question: “How can nurses close the educational gaps for older adults diagnosed with hypertension by addressing the risk factors in the primary care setting?” A comprehensive search of several databases retrieved 19 primary sources that provided evidence related to hypertension education for older adults. The following themes emerged from the research: hypertension education, knowledge translation tools, barriers, cultural sensitivity and inclusivity, therapeutic interpersonal relationships, and a collaborative approach. A gap analysis was conducted in Prince George, British Columbia. The gap analysis further solidified the evidence found in the primary research. Recommendations for enhancing education, practice, and future research are discussed concerning hypertension education based on nurse encounters with older adult patients in the primary care setting. The findings from this project have important implications for supporting the nurse’s educational role in the treatment and management of hypertension among the older adult population.,
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".