“Is This Thing On?: Measuring Technology Self-Efficacy Influence on Cardiac Rehabilitation Patients’ Adoption of a Virtual Care Platform
Bibliographic record
Abstract
Background: The COVID-19 pandemic accelerated the adoption of virtual cardiac rehabilitation (vCR) delivery models. Understanding patient-level factors, such as technology self-efficacy (SE), is crucial for enhancing vCR adoption and ensuring its long-term sustainability. However, no validated tool exists to assess technology SE specifically for vCR. This paper outlines the initial phase of a quality-improvement project focused on developing a survey to assess technology SE among patients with access to videoconferencing (VC) technology in a vCR program. Methods: A 30-item technology SE survey was developed by adapting items from validated instruments to prospectively assess technology SE in vCR and was tested for internal consistency. Results: = 0.009). Although no significant differences were found in overall self-reported technology skills, novel technology use SE, or healthcare technology-related attitudes, VC attendees scored significantly higher on a measure of healthcare technology SE and demonstrated greater confidence in tasks such as opening a Web browser, clicking hyperlinks, downloading apps, and using novel technologies. Conclusions: This quality-improvement initiative highlights disparities in technology SE that may impact participation in vCR programs. Addressing these gaps through targeted screening and interventions could enhance vCR accessibility and equity. Future research should focus on validating SE tools modified for vCR settings and exploring associated interventions to improve technology SE and patient vCR adoption.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".