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Record W2318170788 · doi:10.1097/hcr.0000000000000089

Ethnocultural Diversity in Cardiac Rehabilitation

2014· review· en· W2318170788 on OpenAlexaboutno aff
Liz Midence, Ana Mola, Carmen M. Terzic, Randal J. Thomas, Sherry L. Grace

Bibliographic record

VenueJournal of Cardiopulmonary Rehabilitation and Prevention · 2014
Typereview
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)MedicineWorkforceRehabilitationGerontologyDiseaseFamily medicinePhysical therapyPolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.951
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.387
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations16
Published2014
Admission routes1
Has abstractyes

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