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Record W4214710756 · doi:10.1186/s12913-022-07667-2

Assessing the quality of cardiac rehabilitation programs by measuring adherence to the Australian quality indicators

2022· article· en· W4214710756 on OpenAlexaboutno aff
Carolyn Astley, Alline Beleigoli, Rosanna Tavella, Jeroen Hendriks, Celine Gallagher, Rosy Tirimacco, Gail Wilson, T.O. Barry, Robyn Clark

Bibliographic record

VenueBMC Health Services Research · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilRoyal Australian College of General PractitionersFlinders UniversityMedical Research CouncilCardiac Society of Australia and New ZealandNational Heart Foundation of AustraliaAustralian Government
KeywordsMedicineInterquartile rangeAccreditationRehabilitationMetropolitan areaCanadian Cardiovascular SocietyQuality managementHealth services researchPublic healthFamily medicinePhysical therapyNursingMedical educationOperations managementSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Every year, over 65,000 Australians experience an acute coronary syndrome (ACS) and around one-third occur in people with prior coronary heart disease. Cardiac rehabilitation (CR) aims to prevent a repeat ACS by supporting patients' return to an active and fulfilling lifestyle. CR programs are efficacious, but audits of clinical practice show variability of program delivery, which may compromise patient outcomes. Core components, quality indicators and accreditation of programs have been introduced internationally to increase program standardisation. With Australian quality indicators (QIs) for cardiac rehabilitation recently introduced, we aimed to conduct a survey in one state of Australia to assess the extent to which programs adhere to the measurement of QIs comparing country, metropolitan, telephone and face to face programs. METHODS: A cross- sectional survey design with face validity testing was used to formulate questions to evaluate cardiac rehabilitation program and personnel characteristics and QI adherence. Between October 2020- December 2021, 23 cardiac rehabilitation programs across country and metropolitan areas were invited to participate. Quality improvement was defined as adherence to the Australian Quality Indicators, and we developed an objective score to calculate program performance categorised by quartiles. Significance of CR completion and time to enrolment between program type (telephone versus face to face) and location (country versus metropolitan were compared using Pearson's Chi-square and Mann-Whitney U tests. RESULTS: Among the 23 CR programs, 15 were country and 8 metropolitan-based and 22 were face to face and 1 telephone-based. Median wait time from discharge was 27.0 days, (interquartile range 19.3-46.0) across all programs and country completions of enrolled were 76.9% versus metropolitan 56.5%, p < 0.001 and telephone versus face to face 92.9% versus 59.6% p < 0.001. Pre-program QI adherence was higher than post program for depression, medication adherence, health-related quality of life and comprehensive re-assessment. Seventy four percent of programs were ranked at a medium level of performance (mean score: 11.4/16, SD ± 0.79). CONCLUSIONS: A survey of 23 cardiac rehabilitation programs, showed variability in adherence to measurement of the Australian Cardiovascular and Rehabilitation Association and Australian Heart Foundation Cardiac Rehabilitation Quality Indicators. TRIAL REGISTRATION: Australia New Zealand Clinical Trials Registry (ANZCTR), ACTRN12621000222842 , registered 03/03/2021.

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)0.013
metaresearch head score (Gemma)0.026
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.232
GPT teacher head0.559
Teacher spread0.327 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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