Improving cardiovascular health with motivational interviewing: A nurse practitioner perspective
Bibliographic record
Abstract
PURPOSE: The purpose of this article is to provide nurse practitioners (NPs) with an evidence-based counseling strategy for motivating patients to adopt healthier cardiovascular lifestyles and reduce their cardiovascular disease (CVD) risk. A comprehensive overview of motivational interviewing (MI), framed within the context of the transtheoretical model of change (TTM), demonstrates how primary care NPs can utilize this counseling approach to optimize cardiovascular outcomes in their patients. DATA SOURCES: Published original research and review articles in scholarly journals on the following topics: MI; advice giving; counseling techniques; TTM; CVD; quality NP care. CONCLUSIONS: Although the major risk factors for CVD are largely preventable, CVD rates are increasing to epidemic proportions. Traditional advice giving to decrease CVD risk is minimally effective. MI combined with TTM is an effective counseling technique, which motivates patients who are resistant and ambivalent to change. MI is an appropriate, evidence-based strategy to promote cardiovascular health. IMPLICATIONS FOR PRACTICE: NPs working in primary care can integrate MI with TTM into ongoing patient encounters to facilitate positive behavior changes in their patients over time. Thus, NPs can play a key role in decreasing the growing burden of CVD in North America.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".