Conceptual framework for research and clinical practice concerning cardiovascular health-related behaviors
Bibliographic record
Abstract
OBJECTIVE: To present a conceptual framework based on the PRECEDE model conceived to guide research and the clinical practice of nurses in the clinical follow-up of patients with cardiovascular diseases. METHOD: The conceptual bases as well as the study designs used in the framework are discussed. The contextualization of the proposed structure is presented in the clinical follow-up of hypertensive patients. Examples of the intervention planning steps according to the intervention mapping protocol are provided. RESULTS: This conceptual framework coherently and rationally guided the diagnostic steps related to excessive salt intake among hypertensive individuals, as well as the development and assessment of specific interventions designed to change this eating behavior. CONCLUSION: The use of this conceptual framework enables a greater understanding of health-related behaviors implied in the development and progression of cardiovascular risk factors and is useful in proposing nursing interventions with a greater chance of success. This model is a feasible strategy to improve the cardiovascular health of patients cared for by the Brazilian Unified Health System.
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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 | 0.093 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".