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Record W7046273395

Development and Evaluation of the International Council of Cardiovascular Prevention and Rehabilitation (ICCPR) Program Certification for Low-Resource Settings

2023· article· en· W7046273395 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsCertificationRehabilitationAccreditationPublic healthHealth careQuality (philosophy)Steering committee
DOInot available

Abstract

fetched live from OpenAlex

Karam I Turk-Adawi,1 Usra Elshaikh,1 Aashish Contractor,2 Farzana Amir Hashmi,3 Emma Thomas,4 Fabbiha Raidah,5 Sherry L Grace5,6 1Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar; 2Rehabilitation and Sports Medicine, Sir H.N. Reliance Foundation Hospital, Mumbai, India; 3Preventive Cardiology and Rehabilitation, Tabba Heart Institute, Karachi, Pakistan; 4Centre for Online Health, Centre for Health Services Research, the University of Queensland, Brisbane, Queensland, Australia; 5Faculty of Health, York University, Toronto, Ontario, Canada; 6KITE - Toronto Rehabilitation Institute & Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, CanadaCorrespondence: Karam I Turk-Adawi, Department of Public Health, College of Health Sciences, Qatar University, P.O. Box: 2713, Doha, Qatar, Tel +974 4403 7508, Fax +974 4403 4801, Email kadawi@qu.edu.qaBackground: Cardiac rehabilitation (CR) is a proven model of secondary prevention, but new sites, providing quality care, are needed in low-resource settings. This study (1) described the development of International Council of Cardiovascular Prevention and Rehabilitation’s (ICCPR) Program Certification and (2a) tested its implementation, considering (b) appropriateness of quality standards for these settings.Methods: The Steering Committee finalized 13 standards, requiring 70% be met. They are assessed initially through International CR Registry (ICRR) program survey and patient data; if Certification appears possible, a two-hour virtual site assessment is arranged to corroborate. Standard operating procedures for Assessor training were developed. A multi-method pilot study was then undertaken with a quantitative (description of quality indicators) and qualitative (focus groups on MS Teams) component. ICRR sites with post-program data by April 2022 were invited to participate. Two team members independently analyzed focus group transcripts, using a deductive-thematic approach with NVIVO.Results: Five CR programs from the Eastern Mediterranean, South-East Asian and American regions participated. Upon application, with some data cleaning, initially four programs were eligible to proceed to virtual site assessment. Ultimately, all five programs were certified, each meeting a minimum of 12/13 standards (peak MET increase and program completion rate were not met by some centres). Four themes resulted from the two focus groups of 13 site data stewards: motivation and benefits (eg, international recognition, additional program resources), logistics (eg, communication, cost, site visit process), the standards and their assessment (eg, balance of rigor and feasibility), and suggestions for improvement (eg, website).Conclusion: ICCPR’s Program Certification has been demonstrated to be feasible, rigorous, and acceptable Standards are attainable in low-resource settings. Certified programs reap benefits including additional resources. This first international Certification is suitable for low-resource settings, to complete that from the American and European CR Societies.Keywords: quality of care, Certification, cardiac rehabilitation, low- and middle-income countries, registries, cardiovascular diseases

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 imitation

Not 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.

metaresearch head score (Codex)0.064
metaresearch head score (Gemma)0.093
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.093
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0040.002
Open science0.0040.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.307
GPT teacher head0.517
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

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