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Record W4318667065 · doi:10.1016/j.ahr.2023.100119

COVID-19 anxiety: The impact of older adults’ transmission of negative information and online social networks

2023· article· en· W4318667065 on OpenAlexaffabout
Linying Dong, Lixia Yang

Bibliographic record

VenueAging and Health Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCasualSocial capitalAnxietySocial isolationCoronavirus disease 2019 (COVID-19)PsychologyPandemicBridging (networking)Social psychologyGerontologyDevelopmental psychologyPsychiatryMedicinePolitical scienceInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Objectives: This study examines the impact of older adults' online social networks on their COVID-19 anxiety, directly or indirectly through social transmission of negative information about COVID-19. Social networks were indexed by both bonding capital (i.e., social relationships formed with family and friends) and bridging capital (i.e., social relationships formed through casual social networks). Methods: An on-line survey was conducted with 190 older adults who were in self-isolation in Ontario in the early waves of the COVID-19 pandemic in 2020. Results: Bonding and bridging capital showed different impacts on older adults' informational behavior and COVID-19 anxiety. While bonding capital deterred older adults from transmitting negative COVID-19 information and thus reduced COVID-19 anxiety, bridging capital contributed to increased dissemination of negative information and thus heightened older adults' anxiety. Discussion: Our findings shed light on the detrimental behavioral and psychological impact of casual online social networks on older adults amidst a public health crisis.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.132
GPT teacher head0.516
Teacher spread0.384 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations23
Published2023
Admission routes2
Has abstractyes

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