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Record W4312917061 · doi:10.1051/shsconf/202214803001

Interview-based Study about the Impact of the COVID-19 Pandemic on Smartphone Use among the Seniors in China’s Firsttier Cities

2022· article· en· W4312917061 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSHS Web of Conferences · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPandemicChinaCoronavirus disease 2019 (COVID-19)Social mediaSocial distanceInternet privacyAdaptabilityPsychologyBusinessPolitical scienceMedicineComputer science

Abstract

fetched live from OpenAlex

The outbreak of the COVID-19 pandemic was a sudden disaster for all human beings. To prevent the spread of the pandemic, China used smart facilities to manage it, especially relying on smartphones. This study examines what the impact of the pandemic is on the use of smartphones by seniors, a group that is weaker in the use of smart devices. The study looks at the situation with seniors in the new media environment, seeking to help them cross the digital divide and bring social attention to their plight during the pandemic. The authors conducted in-depth interviews with 52 seniors from first-tier cities in China and then did a discourse analysis of the interviews. The study found that the pandemic increased the smartphone penetration among the seniors, and helped them mitigate the digital divide and increase their social adaptability. However, it is still noteworthy for smartphone addiction.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.337
Teacher spread0.278 · 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