Navigating the Pathway to Co-designed Nurse Practitioner Research in Aotearoa New Zealand
Bibliographic record
Abstract
Nurse practitioners (NPs) are an integral part of New Zealand's health care system, addressing workforce shortages and seeking to reduce health inequities. Despite their increasing presence, there remains limited evidence on patient, health service, and economic outcomes of NP-led care. Te Tiriti o Waitangi (Treaty of Waitangi), as New Zealand's foundational document, establishes obligations for equity and partnership with Māori (indigenous people of New Zealand), yet considerable health care disparities persist. This manuscript presents a co-designed research approach that emphasizes collaboration between Māori NPs and leaders and non-Māori using a noho marae (staying or living on a marae) approach. The noho marae is an immersive, culturally embedded practice ensuring that the research aligns with Te Tiriti o Waitangi obligations and Māori priorities. The noho marae created a culturally safe environment for relationship building, collective decision-making, and discussion about a research agenda that honors Māori leadership and self-determination (rangatiratanga). Key learnings from the co-design process underscore the importance of culturally responsive research methods, highlighting how such partnerships strengthen health care research, workforce development, and health equity initiatives. This report provides insights into co-design approaches for indigenous health research, particularly in contexts without formal treaty obligations. This may be useful globally in countries such as Australia, Canada, and the United States. It reinforces the need for sustained investment in culturally safe and sensitive research to ensure equitable health care outcomes and meaningful systemic change.
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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 | 0.190 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".