Nature and delivery of cardiac rehabilitation in New Zealand: are services equitable to other high-income countries?
Bibliographic record
Abstract
AIMS: To compare the nature and delivery of cardiac rehabilitation (CR) services within New Zealand by island (North vs South; NI, SI), and to other high-income countries (HICs). METHODS: In this cross-sectional study, secondary analysis of an online survey of CR programmes globally was undertaken. Results from New Zealand were compared to data from other HICs with CR. RESULTS: Twenty-seven (62.7%) out of 43 CR programmes in New Zealand (n=18/31, 66.7% respondents from NI) and 619 (43.1%) from 28 other HICs completed the survey. New Zealand CR programmes offered a median of 16.0 sessions/patient (interquartile range (IQR)=12.0-36.0; vs 21.6 sessions in other HICs, IQR=12.0-36.0, p=0.016), delivered by a team of 6.0 staff (IQR=5.5-7.0; vs 7.0 staff; IQR=5.0-9.0, p=0.012). New Zealand programmes were significantly less comprehensive than other HICs (p=0.002); within New Zealand, NI programmes were more likely to provide an initial and end-of-programme assessment, supervised exercise training and depression screening, compared to SI programmes (all p<0.05). New Zealand more often offered CR in an alternative setting (n=14, 58.3%), compared to other HICs (n=190, 36.5%), p=0.03). CONCLUSIONS: CR programmes in New Zealand offer fewer sessions and have fewer elements compared to other HICs, and disparity exists in programmes across New Zealand. More investment is needed to ensure CR in New Zealand meets international guidelines.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".