COVID-19 Pandemic: Wake-up Call and Accelerator for Cardiac Rehabilitation
Bibliographic record
Abstract
Cardiac rehabilitation (CR) is a cornerstone of secondary prevention in cardiovascular care, improving survival, reducing rehospitalization, and enhancing quality of life. Despite robust evidence and strong guideline support, CR remains markedly underutilized in Canada and globally, with significant disparities by sex, race, geography, and socioeconomic status. The COVID-19 pandemic disrupted more than three-quarters of CR programs worldwide, exposing deep-rooted limitations in access, infrastructure, and delivery models. At the same time, the pandemic served as a catalyst for innovation. Rapid implementation of virtual, home-based, and hybrid models demonstrated that CR could be delivered flexibly and effectively beyond traditional settings. This review synthesizes emerging evidence and policy responses, highlighting opportunities to modernize CR delivery while embedding equity, patient-centeredness, and digital innovation into routine care. We conclude that the future of CR must be inclusive, technology-enabled, and integrated into the broader continuum of preventive care. The lessons of the pandemic offer a roadmap-and a renewed imperative-to close longstanding gaps and reimagine cardiac rehabilitation for all who need it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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".