The 2022 restructure of Aotearoa New Zealand's health system: Will it succeed in advancing equity where others have failed?
Bibliographic record
Abstract
Aotearoa New Zealand has restructured its health system with the objective of addressing inequitable access to health services and inequitable health outcomes, particularly those affecting the indigenous Māori population. In July 2022, two new organisations were created to centralise planning, funding and provision responsibilities for publicly funded health services in Aotearoa New Zealand. Health New Zealand and the Māori Health Authority have been created to drive transformational change within the national health system and monitor and improve the health and wellbeing of Māori. At the local level, new Localities are to be formed with the aim of integrating services between government and non-government health and social services providers, while incorporating local Māori and local communities in co-design of services. These changes will be of interest to those in many other countries who are grappling with their own colonial histories and struggling to provide health services in ways that are equitable and contribute to positive health outcomes for their whole population. Although key aspects of the reforms are well supported within the health sector, the ambitious scope and timing of their introduction in the context of the COVID-19 pandemic and health workforce shortages can be expected to generate significant implementation challenges.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".