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Record W4416695502 · doi:10.62787/mhm.v3i4.254

Bridging the Digital Divide: Senior Chinese Immigrants’ Experiences with Health Technologies in Canada

2025· article· W4416695502 on OpenAlexaffabout
Zhenyi Li, Xiaonan Zhang, Xinwei Wang, Min Yuan, Weiguo Zhang

Bibliographic record

VenueThe Journal of Medicine Humanity and Media · 2025
Typearticle
Language
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of TorontoRoyal Roads University
Fundersnot available
KeywordsDigital divideDigital literacyBridging (networking)ImmigrationDigital healthFraming (construction)SituatedHealth literacy

Abstract

fetched live from OpenAlex

The global aging population is increasingly impacted by the digital divide, which affects their quality of life and access to services. Chinese senior immigrants in Canada face great challenges navigating digital environments due to language barriers, cultural adaptation issues, and limited access to digital resources. Using Cultural-Historical Activity Theory (CHAT) as a theoretical framework, this study explores the key factors of the digital divide among Chinese senior immigrants, challenges of their use of health-related technologies, and their strategies in adapting to these digital tools. Through semi-structured, in-depth interviews with 9 Chinese senior immigrants in Canada, this study argues that the digital divide is not merely an issue of technological access but a socially situated, culturally mediated, and historically developed phenomenon shaped by social structures, community supports, learning, and well-being. By framing digital literacy as a socially situated and developmental process, this study contributes to health communication by proposing a Cross-Cultural Digital Health Literacy Nexus (CDHLN) that enhances cross-cultural digital health engagement, improves senior immigrants' access to health information, and promotes well-being among aging immigrant populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.269
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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