The Enduring Impact of Social Factors on Exercise Tolerance in Men Attending Cardiac Rehabilitation
Bibliographic record
Abstract
PURPOSE: This study explored the influence of social support on a prognostic indicator among cardiac patients, exercise tolerance. The relationship of sociodemographic factors to social support was examined, as well as the role of social support as a potential mediator between sociodemographic factors and exercise tolerance. METHODS: Archival data were collected from a sample of 254 men referred to cardiac rehabilitation. An exercise tolerance test was completed upon entry into cardiac rehabilitation, after 14 weeks, and after 9 months. RESULTS: Sociodemographic factors and social support reported upon entry into the cardiac rehabilitation program were related to initial and post-cardiac rehabilitation exercise tolerance, after controlling for admitting diagnoses, medical history, smoking, and perceived severity of illness. Overall, 28% of the variance in exercise tolerance was explained at baseline, 19% at 14 weeks, and 20% at 9 months. Specifically, older individuals had poorer exercise tolerance, whereas those with more income had better exercise tolerance. Social support was positively related to exercise tolerance at all 3 times. Older men reported less social support than younger men did, and those with more income reported more social support. However, social support did not mediate the relationship between sociodemographic factors and exercise tolerance. CONCLUSIONS: Results support the potential use of broad social factors in examining the determinants of prognostic factors for heart patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".