Review of the Scientific Literature on Young Adults Related to CardiovascularDisease Intervention
Bibliographic record
Abstract
Many young adults are at risk for cardiovascular disease related to their behavioral choices. Irresponsible alcohol consumption, tobacco smoking, sedentary lifestyle, poor dietary habits, and excessive weight gain are some of the behaviors that put young adults at risk. The Centers for Disease Control and Prevention identified that 15% of young adults are diagnosed with chronic illnesses related to their behavioral choices. The purpose of this review is to identify, in the literature, interventions that are currently available to young adults and evaluate the adequacy and effectiveness of those interventions. An extensive electronic search was conducted using CINAHL, EBSCOhost, Cochrane, PubMed, and Google Scholar. A total of 130 articles were identified and 28 articles met the inclusion criteria. Three main interventions were identified for young adults: personalized interventions, technology-based interventions, and educational/behavioral interventions. The interventions were all effective to different degrees and interventions were most effective when they were combined. This review impacts in what manner nurses and health care providers deliver health promotion, prevention, and management of cardiovascular risk factors in young adults; in particular, nurses play a key role in lifestyle modifications including diet and exercise.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".