Lifestyle Knowledge and Behavior Among Stroke and High-Risk Younger Adult Patients Through Sex, Age and Stroke Status Differences: A Cross-Sectional Study
Bibliographic record
Abstract
Background: The prevalence of stroke is projected to rise over the next 30 years, particularly among younger adults (≤65 years of age). Stroke is associated with modifiable risk factors, highlighting the importance of risk factor modification. However, to modify risk factors, it is important to understand younger adult stroke and high-risk patients’ lifestyle-related knowledge, behaviors and associated facilitators and barriers, which this study aimed to address with attention to sex, age, and stroke status-related differences. Methods: A cross-sectional study was conducted. Data were collected through an online self-reported survey. Descriptive and inferential statistics were conducted with attention to sex, age, and stroke status differences. Results: A total of 104 participants comprised the sample. Variability in lifestyle-knowledge was found. Most participants ate processed food, moderately exercised, slept <7 hours per night, had a sense of social connectedness, and moderate-to-manageable stress. Emotions, social and family responsibilities influenced diet and exercise. Sex, age, and stroke status differences were observed. Conclusions: Findings have implications on the development of lifestyle medicine prescriptions and interventions as standard of care to support brain health and reduce the risk of stroke and/or its reoccurrence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".