A study of physically active versus inactive persons living with congestive heart failure during the covid-19 pandemic
Bibliographic record
Abstract
Covid-19 presented tremendous challenges to healthcare systems throughout the world. In particular, the presence of comorbid conditions became a significant factor due to the greatly increased risk of hospitalization and death in people living with diseases such as Congestive Heart Failure. While the literature has long indicated relationships between psychological challenges (depression and anxiety), the pandemic represented a particular challenge due to the way that it limited individual’s ability to engage in activities outside of the home. While all activities were limited, exercise presented a particular challenge as it is so essential to Congestive Heart Failure self-management. The current study used a quantitative descriptive design to examine the relationship between psychological variables and heart failure self-management. The study indicated relatively mild alterations in depression and anxiety. However, the results indicated a significant relationship between physical activity during the pandemic and having engaged in Cardiac Rehabilitation prior to the lockdowns (p < .05). Further, the results indicated that self-efficacy related to self-management was higher in patients who engaged in higher activity levels (p < .001). The study supported the importance of cardiac rehabilitation and subsequent exercise in establishing self-efficacy and beneficial outcomes in patients living with Congestive Heart Failure.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".