Factors Hindering Cardiac Rehabilitation in Low- and Middle-Income Countries, by Level and Setting
Bibliographic record
Abstract
Cardiovascular diseases are among the leading causes of death and disability globally, with the greatest burden in low- and middle-income countries (LMIC).1 Cardiac rehabilitation (CR) mitigates this growing epidemic.2 Despite this, CR is underutilized.3 This is particularly so in LMIC where it is needed most, availability is scant, and there are greater challenges to implementation.4 Barriers to CR delivery are multifactorial, with factors at play at the health system, referring provider, CR program, as well as patient levels.3 These have been well-characterized in high-resource settings, with some review in LMIC,4 although the latter is dated given recent contextual changes. One of the main recommendations to improve CR use has been availability of unsupervised (ie, remote, home-based) models.5 Given the high penetrance of mobile phones in LMIC, programs have more recently initiated technology that is quite advanced and patient-friendly. In response to the COVID-19 pandemic in these countries, there has been a great shift to online CR care,6 with need for more. While characterized for high-resource settings,7,8 factors hindering CR availability and utilization that are unique to, or more problematic in, LMIC have not been described, particularly by level and setting. Based on a rapid review of literature and the expertise of the International Council of Cardiovascular Prevention and Rehabilitation (ICCPR; 43 member associations and 17 “friends,” of which 52% are from LMIC), this Infographic illustrates these factors. This brief version, as well as the full version (see Supplemental Digital Content, available at: https://links.lww.com/JCRP/A457), separately displays factors hindering supervised and unsupervised CR delivery in LMIC, at the societal, referring clinician, CR program, and patient levels. Ideally, unsupervised CR models do involve some in-person sessions at least at the start of a program, to enable full assessment, risk stratification, plan of care development, and therapeutic rapport. There is now burgeoning research on hybrid CR (ie, combining supervised center-based and remote/unsupervised) in high-resource settings, with corresponding best practice recommendations for implementation.9 While there are also sound recommendations to promote supervised CR implementation in LMIC,4 and some training available from the ICCPR on supervised and unsupervised delivery,10 it is hoped strategies to overcome barriers to unsupervised CR delivery in LMIC will be identified as well. Ultimately, we must support CR champions in LMIC to address these multilevel barriers to CR delivery, to realize availability of CR in all settings, based on context and patient need, in both high and LMIC.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".