The development and testing of a nurse practitioner secondary prevention intervention for patients after acute myocardial infarction: A prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Patients with acute myocardial infarction (AMI) are at high risk for reinfarction and death. Therapies that have been shown to reduce these risks (secondary prevention) continue to be underutilized. Nurse practitioners are well positioned to provide secondary prevention during and following hospitalization. OBJECTIVES: The purpose of this study was to evaluate the effects of NP care on the rate of provider implementation and patient achievement of evidence-based secondary prevention target goals. DESIGN: A prospective cohort design was used, which compared achievement of target goals between patients who received secondary prevention care from an NP to those who received usual care. PARTICIPANTS: The sample consisted of 65 patients with AMI, admitted to a large community hospital. Patients meeting eligibility criteria were recruited consecutively. METHODS: The intervention was delivered by the NP before discharge from hospital and one week, two weeks, six weeks and 3 months after discharge. Data on patients' achievement of goals were obtained before discharge from hospital and 3 months after discharge from both groups. RESULTS: This study's results provide preliminary evidence that an NP delivered secondary prevention intervention can significantly improve achievement of the following target goals when compared to usual care: smoking cessation (OR 5), blood pressure (OR 15), attendance at cardiac rehabilitation (OR 7), physical activity five days a week (OR 17), physical activity ≥ five days a week (OR 34), achieving a glycated haemoglobin < 7% in those with diabetes (OR 10), triglyceride levels (p = .02), statin use at follow-up (p = .05), and number of weeks to cardiac rehabilitation (p = .05). CONCLUSION: NP-led interventions such as this warrant duplication to evaluate reproducibility of the intervention and to determine if short-term improvements in secondary prevention goals translate into morbidity and mortality benefits.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".