User Experiences of the NZ COVID Tracer App in New Zealand: Thematic Analysis of Interviews
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Résumé
BACKGROUND: For mobile app-based COVID-19 contact tracing to be fully effective, a large majority of the population needs to be using the app on an ongoing basis. However, there is a paucity of studies of users, as opposed to potential adopters, of mobile contact tracing apps and of their experiences. New Zealand, a high-income country with western political culture, was successful in managing the COVID-19 pandemic, and its experience is valuable for informing policy responses in similar contexts. OBJECTIVE: This study asks the following research questions: (1) How do users experience the app in their everyday contexts? and (2) What drives the use of the app? METHODS: Residents of New Zealand's Auckland region, which encompasses the country's largest city, were approached via Facebook, and 34 NZ COVID Tracer app users were interviewed. Interview transcripts were analyzed using thematic analysis. RESULTS: Interviews ranged in duration from 15 to 50 minutes. Participants ranged in age from those in their late teens to those in their early sixties. Even though about half of the participants identified as White New Zealanders of European origin, different ethnicities were represented, including New Zealanders of South Pacific, Indian, Middle Eastern, South American, and Southeast Asian descent. Out of 34 participants, 2 (6%) identified as Māori (Indigenous New Zealanders). A broad range of careers were represented, from top-middle management to health support work and charity work. Likewise, educational backgrounds ranged broadly, from high school completion to master's degrees. Out of 34 participants, 2 (6%) were unemployed, having recently lost their jobs because of the pandemic. The thematic analysis resulted in five major themes: perceived benefits, patterns of use, privacy, social influence, and need for collective action. Benefits of using the app to society in general were more salient to the participants than immediate health benefits to the individual. Use, however, depended on the alert level and tended to decline for many participants at low alert levels. Privacy considerations played a small role in shaping adoption and use, even though the participants were highly aware of privacy discourse around the app. Participants were aware of the need for high levels of adoption and use of the app to control the pandemic. Attempts to encourage others to use the app were common, although not always successful. CONCLUSIONS: Appeals to civic responsibility are likely to drive the use of a mobile contact tracing app under the conditions of high threat. Under the likely scenario of COVID-19 remaining endemic and requiring ongoing vigilance over the long term, other mechanisms promoting the use of mobile contact tracing apps may be needed, such as offering incentives. As privacy is not an important concern for many users, flexible privacy settings in mobile contact tracing apps allowing users to set their optimal levels of privacy may be appropriate.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| 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 découle