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
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».