Social Networks and Exercise in Coronary Heart Disease Patients
Bibliographic record
Abstract
PURPOSE: Coronary heart disease (CHD) is a leading cause of death in the Western world. Although the benefits of exercise as a health behavior are widely known, the majority of CHD patients fail to adhere to an exercise program. The availability of social networks has been shown to be related to health behaviors; however, the impact that social networks have on CHD patient exercise involvement is not well understood. The aim of this study was to investigate the role social networks, defined as the number and source of people patients lived with, plays on exercise involvement in CHD patients. METHODS: A total of 756 cardiac outpatients (236 women and 520 men) were recruited. Presence, source, and size of patient social networks and exercise (total leisure-time physical activity) were assessed via a questionnaire. RESULTS: There was no difference in exercise involvement, as measured in metabolic equivalents of task hours per week, between those patients living with at least 1 other person and those people who lived alone (M = 7.53, SD = 0.50, and M = 8.49, SD = 1.07, respectively; F = 0.65, P = .422). However, there was a significant difference between patients who currently lived with a child compared with those who did not live with a child (F = 6.98, P = .008). Patients with children engaged in less exercise than those who did not live with a child (M = 5.41; SD = 0.97 vs M = 8.46; SD = 0.53). CONCLUSIONS: Considering that only living with children, rather than living with any other individual, seemed to affect patient exercise involvement, further research is needed to investigate the social mechanisms underlying this relationship.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".