The health of New Zealand cardiology: senior medical officer workforce survey
Bibliographic record
Abstract
AIM: To quantify the current state of the cardiology specialist workforce in Health New Zealand - Te Whatu Ora. METHODS: The Cardiac Society of Australia and New Zealand sent a survey to all Health New Zealand - Te Whatu Ora cardiology departments in 2024, requesting information on specialist cardiac staff. Population information was obtained from Health New Zealand - Te Whatu Ora. International comparisons were obtained by website search. RESULTS: Of 154 Health New Zealand - Te Whatu Ora-employed cardiologists, 119 (77%) were male, and 113 (73%) received cardiology training in New Zealand. Over half were aged >50, 35% >55, including 18% >60 years. Time in current position was 12±9 years and the vacancy rate was 14%. The current ratio of persons per cardiologist is 35,000. In the five districts with the highest proportion of Māori and Pacific peoples, this ratio exceeds the national average: Tairāwhiti 54,000; Counties Manukau 38,000; Lakes 61,000; Northland 52,000; Hawke's Bay 47,000. For cities with cardiac surgery the ratio is 32,000 and without is 46,000. International ratios include: United States of America (USA) 15,000; Canada 25,000; United Kingdom (UK) 40,000 and Australia 25,000 persons per cardiologist. CONCLUSIONS: Health New Zealand - Te Whatu Ora has an experienced but ageing cardiologist workforce, with many vacancies. Districts with higher Māori/Pacific populations have fewer cardiologists per capita than the national average of 1:35,000, which is similar to the UK, but less than the USA, Australia and Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".