An Exploration of the Goodness of Fit of Web-Based Tools for Māori: Qualitative Study Using Interviews and Focus Groups
Bibliographic record
Abstract
BACKGROUND: Indigenous communities often have poorer health outcomes and services under traditional models of care. In New Zealand, this holds true for Māori people who are tāngata whenua (the indigenous people). Several barriers exist that decrease the likelihood of indigenous communities often have poorer health outcomes and poor service fit under traditional models of care, including access issues, systemic and provider racism, and a lack of culturally safe and responsive services. Web-based interventions (WBIs) have been shown to be effective in supporting mental health and well-being and can overcome some of these barriers. Despite the large number of WBIs developed, more investigation is needed to know how well WBIs fit with an indigenous worldview and how they meet the needs of indigenous communities so that a digitally based future does not drive social and health inequities. OBJECTIVE: This study aims to explore the goodness-of-fit of WBIs of Māori individuals, the indigenous people of Aotearoa/New Zealand. METHODS: We used interviews (n=3) and focus groups (n=5) with 30 Māori participants to explore their views about WBIs. Interviews were analyzed using reflexive thematic analysis by members of the research team. RESULTS: Overall, there was a perception that the design of WBIs did not align with the Māori worldview, which centers around people, relationships, spirituality, and holistic views of well-being. A total of 4 key themes and several subthemes emerged, indicating that WBIs were generally considered a poor fit for Māori. Specifically, the themes were as follows: (1) WBIs are disconnected from the core values of te ao Māori (the Māori worldview), (2) WBIs could be helpful in the right context, (3) there are significant barriers that may make it harder for Māori to use WBIs than other groups, and (4) ways to improve WBIs to help engagement with Māori. CONCLUSIONS: While WBIs are often considered a way to reduce barriers to care, they may not meet the needs of Māori when used as a stand-alone intervention. If WBIs are continued to be offered, developers and researchers need to consider how to develop WBIs that are responsive and engaging to the needs of indigenous communities rather than driving inequities. Ideally, WBIs should be developed by the people they are intended for to fit with those populations' world views.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".