The Experience and Impact of Digital Technologies on Indigenous Populations in New Zealand During the COVID-19 Pandemic and Cyclone Gabrielle: The Kaupapa Māori Methodology
Bibliographic record
Abstract
Background: Pandemics, such as COVID-19, and climate change-related catastrophic weather events are increasing, impacting social connectedness within communities by disrupting social cohesion, increasing loneliness, and affecting mental health and social well-being. Digital technology, in addition to being used for communication, education, and business transactions, also plays a vital role in maintaining a country's health and well-being, as well as sustaining economic growth. Objective: This study aimed to explore the experiences of Māori kaumātua in using digital technology to meet their health needs within Ngāti Kahungunu, North Island, New Zealand, during the COVID-19 pandemic and Cyclone Gabrielle. Methods: This qualitative study employed the Kaupapa Māori methodology to understand the challenges, resilience, and approaches used by Māori to maintain connectedness and access essential services. An inductive approach to thematic analysis, as recommended by Braun and Clarke, was used to ensure a thorough and robust data analysis. The user characteristic was assessed on a semantic level using the information provided in the narrative text. Results: The findings highlight the role of digital technology in disaster management and underscore the urgent need to address digital disparities in support of vulnerable populations. In this study, 14 individuals were interviewed, comprising 71% (n=10) women and 29% (n=4) men. These participants fell into different age groups, with 9 participants being 65 years or older (older adults). Of the total participants, 43% (n=6) were limited users, 43% (n=6) comprised confident users, and the rest (n=2; 14%) were normal users. A total of 6 themes emerged from the interview data: social connectedness and resilience, digital literacy and access to information, barriers to telecommunications and digital technology, cultural appropriateness and psychological barriers, perceived threats of feeling insecure, and impact on mental health and emotional well-being. Conclusions: Vulnerable situations such as pandemics and extreme weather events can have tremendous effects on the lives of Indigenous people who live remotely. The study also focused on the actions that should be taken to mitigate these challenges and overcome difficult circumstances, such as the pandemic and the cyclone. The recommendations include a better health care system and improved coordination among care providers, user-friendly digital solutions, ensuring local funding and community services, establishing training processes for basic digital skills, and fostering leadership and partnerships with Indigenous New Zealanders.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".