Ethnocultural Diversity in Cardiac Rehabilitation
Bibliographic record
Abstract
Cardiovascular disease is the leading cause of death globally. Despite a greater burden of disease, ethnocultural minorities in both the United States and Canada are significantly less likely to access cardiac rehabilitation (CR). Without equitable access to CR, these patients may be more likely to experience recurrent cardiac events and unnecessarily premature death. In this article, the current state of ethnocultural diversity in CR patients and unique barriers that ethnocultural minority patients face are reviewed. Strategies for CR program delivery and diversity of CR program staff are considered. Guidance on ethnocultural considerations in American and Canadian associations of CR is also reviewed. Lower rates of access to CR are seen among ethnocultural minorities in both American and Canadian CR programs. Only 2 studies evaluating ethnoculturally tailored CR could be identified in the literature. American CR staff are predominantly white (∼96%), whereas ethnocultural data are not collected from Canadian CR professionals. American guidelines emphasize the importance of ethnocultural competency. Meanwhile, Canadian guidelines underscore the low use of CR services among ethnocultural minorities, and support ethnoculturally informed CR delivery. The American and Canadian populations are rapidly diversifying, yet the CR workforce is not, and ethnocultural minorities continue to be underrepresented in our programs. Although recent CR guidelines have made some preliminary recommendations to overcome these discrepancies, more focused efforts are needed. Thirteen points of action are proposed for the CR community with the goal of promoting the development and delivery of more ethnoculturally sensitive CR services.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".