A Modeling Intervention in Heart Failure
Bibliographic record
Abstract
BACKGROUND: Peak oxygen uptake (VO(2)) testing is commonly used to assess chronic heart failure (CHF) patients' exercise tolerance. The test requires maximal effort; however, many participants have low confidence (self-efficacy) to perform optimally. PURPOSE: This randomized controlled trial examined the effectiveness of a modeling intervention to increase Peak VO(2) (PVO(2)) and self-efficacy in people diagnosed with CHF. METHODS: Twenty participants with a diagnosis of CHF were randomized to either an intervention (modeling DVD) or a control group. Both groups completed a measure of self-efficacy prior to performing two PVO(2) tests, each separated by 7 days. After completing the first test (T1) the intervention group watched a 10-min coping model DVD. All participants returned 1 week later (T2) to complete identical study procedures. RESULTS: Analysis of covariance results showed that compared with the participants in the control group, those assigned to the modeling intervention had higher PVO(2) at T2, F (1, 19) = 4.38, p = 0.05, eta (2) = 0.21 and self-efficacy, F (1, 19) = 5.80, p < 0.05, eta (2) = 0.25. Only partial support was found for change in self-efficacy mediating treatment outcome (PVO(2)). CONCLUSIONS: Watching a modeling video is associated with increased PVO(2) and self-efficacy. These results have implications for testing patients in a clinical setting to maximize exercise tolerance test results.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".