Diverse Attitudes and Experiences With Technology Use During the COVID-19 Pandemic Among Asian American and Pacific Islander Adults (the COMPASS Study): Survey Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic forced the world to quarantine to slow the rate of transmission, causing communities to transition into virtual spaces. Asian American and Pacific Islander communities faced the additional challenge of discrimination that stemmed from racist and xenophobic rhetoric in the media. Limited data exist on technology use among Asian American and Pacific Islander adults during the height of the COVID-19 shelter-in-place period and its effect on their physical and mental health. OBJECTIVE: This study aims to examine Asian American and Pacific Islander adults' attitudes, perspectives, and experiences regarding their use of technology during the COVID-19 pandemic. METHODS: We collaborated with community partners and used social media to distribute the COVID-19 Effects on the Mental and Physical Health of Asian Americans and Pacific Islanders Survey Study, a nationwide multilingual survey available in English, Chinese, Korean, Samoan, and Vietnamese. The survey was administered from October 2020 to February 2021, and participants rated their level of agreement (1=not at all to 5=extremely) on 6 items assessing their attitudes toward technology use. Thematic analysis was conducted on responses to the open-ended question "Is there anything else you want to tell us about your use of technology during COVID-19?" The qualitative responses were reviewed, analyzed, coded, and organized into corresponding themes. RESULTS: The mean age of respondents was 45.9 (SD 16.3; range 18-98) years, with 5398 participants completing the quantitative survey and 1115 (20.66%) providing unique responses to the open-ended question. In the quantitative survey, 68% (3671/5398) of the respondents reported being comfortable using technology; the majority indicated that it helped them keep up with the news (4318/5398, 79.99%), maintain social connections (4102/5398, 75.99%), and provide care for others (2537/5398, 46.99%). However, responses were mixed regarding the usefulness of technology for health: 39.99% (2159/5398) agreed that it was helpful for mental health but disagreed regarding physical health. Four main themes emerged from the qualitative analysis: (1) technology was critical for functioning across many aspects of life and maintaining physical, mental, and emotional well-being; (2) technology was often the only means of interpersonal social connections; (3) overuse led to negative physical and mental health outcomes; and (4) technology use was associated with multiple challenges and barriers. CONCLUSIONS: Our findings revealed diverse perspectives and experiences related to technology use by Asian American and Pacific Islander adults during the height of the COVID-19 pandemic. Dependence on technology may have exacerbated social inequities, particularly for those with lack of access to devices and Wi-Fi and limited English proficiency, affecting their ability to work, apply for jobs, and communicate virtually. Further qualitative research would be beneficial in amplifying the perspectives of Asian American and Pacific Islander adults to uncover concerns and address health disparities.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».