Feasibility of Assessing 2 Cardiac Rehabilitation Quality Indicators
Bibliographic record
Abstract
In Brief PURPOSE: The Canadian Cardiovascular Society initiated a pan-Canadian process for development of quality indicators (QIs) for cardiac rehabilitation (CR). Before implementation, the QIs underwent pilot testing to ensure they were acceptable and feasible for field implementation. The objectives of this test were to assess (1) the technical feasibility of measuring the QIs; (2) the workload required to measure the QIs; and (3) acceptability of measuring the QIs and issues with their implementation. METHODS: The 2 indicators chosen for field testing were QI-1 (% of eligible inpatients referred) and 2b (median wait time from CR referral receipt to enrollment). The approach consisted of 3 steps: (1) data extraction to test technical feasibility; (2) completing a workload diary; and (3) providing input through a semistructured interview regarding acceptability and implementation issues. Three academic CR sites were selected to undertake the field test. RESULTS: QI-1 ranged from 51.0% to 68.4%, and QI-2b was reported as 27 days (median) by one site, and 22 days (mean) by another. It was not considered feasible for CR programs to assess all potentially CR-eligible inpatients for CR referral exclusions. Compilation required 4.2 hours for QI-1 and 1.8 hours for QI-2b. QI assessment was acceptable to the programs, but changes in practice would be needed at each site to implement the QIs. CONCLUSIONS: CR programs may require enhancement of information-tracking processes to enable QI measurement. It was recommended that the QIs be implemented, but should undergo minor revisions to enhance feasibility. Two Canadian cardiac rehabilitation (CR) quality indicators (QI), namely inpatient referral and wait time, underwent pilot-testing for acceptability and implementation feasibility. QI assessment was highly acceptable to the academic CR community. There were issues raised regarding inclusions and exclusions for the first QI, which should be addressed before national roll-out.
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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 | 0.102 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".