Case study: COVID-19 and governing for health and wellbeing in New Zealand – putting communities at the centre
Bibliographic record
Abstract
Introduction We have learned tough lessons from the COVID-19 pandemic about trust in government and that when our systems are disrupted, many communities are immediately vulnerable to hardship (Henrickson, 2020). With the government poised to make significant changes to the health system there is an opportunity in New Zealand (NZ) to revisit our social infrastructure and put communities at the centre of decision-making. An important part of this infrastructure is the untapped potential of local government and local iwi (Māori tribes) to strengthen the ability for communities to act on their own needs. Understanding the history, legislative context and current challenges and opportunities for local governance, provides direction for how community and public health action could be more impactful and sustainable. For decades, public health has had community health and empowerment at its theoretical core. The 1986 Ottawa Charter for Health Promotion is a widely used framework for action to improve health (World Health Organization, 1986). Two central elements are a greater focus on community action on health and to reorient health services towards patients and communities. This built on the 1978 Alma Ata declaration on primary healthcare which also called for communities to be central actors within the health system (World Health Organization, 1978). In 2008 the World Health Organization convened a Commission on the Social Determinants of Health which found, while the health system itself is one determinant, most of health and illness results from interactions with, and within, the places in which we are born, live, work, play and age (Commission on the Social Determinants of Health, 2008). And this is no different in NZ. We have a rising burden of non-communicable diseases (NCDs; heart diseases, cancers, mental health disorders) which are linked to shared risk factors found in our surrounding social and physical environments (Ministry of Health, 2013). NCDs are also the biggest drivers of health inequality, but as COVID-19 has shown, infectious diseases will also follow patterns of existing inequality if given the opportunity to spread. For 50 years we have known in NZ of significant health inequities by ethnic group – in particular Māori and Pacific people (Blakely et al, 2005).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".