Navigating the Pathway to Co-designed Nurse Practitioner Research in Aotearoa New Zealand
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it