Hā Ora: Reflecting on a Kaupapa Māori Community-Engaged Co-design Approach to Lung Cancer Research
Bibliographic record
Abstract
Co-designed research is gaining prominence within the health care space. Community engagement is a key premise of co-design and is also particularly vital when carrying out kaupapa Māori research. Kaupapa Māori describes a “by Māori, for Māori” approach to research in Aotearoa/New Zealand. This article discusses the research process of Hā Ora: a co-design project underpinned by a kaupapa Māori approach. The objective was to explore the barriers to early presentation and diagnosis of lung cancer, barriers identified by Māori. The team worked with four rural Māori communities, with whom we aimed to co-design local interventions that would promote earlier diagnosis of lung cancer. This article highlights and unpacks the complexities of carrying out community- engaged co-design with Māori who live in rural communities. In particular, we draw attention to the importance of flexibility and adaptability in the research process. We highlight issues pertaining to timelines and budgets, and also the intricacies of involving co-governance and advisory groups. Overall, through this article, we argue that health researchers need to prioritise working with and for participants, rather than on them, especially when working with Māori communities.
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.
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 | 0.049 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".