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Record W4205293711 · doi:10.2196/33029

The Moderating Role of Community Capacity for Age-friendly Communication in Mitigating Anxiety of Older Adults During the COVID-19 Infodemic: Cross-sectional Survey

2022· article· en· W4205293711 on OpenAlexvenueno aff
Frankie Ho Chun Wong, Dara Kiu Yi Leung, Edwin Lok Yan Wong, Tianyin Liu, Shiyu Lu, On Fung Chan, Gloria Hoi Yan Wong, Terry Yat Sang Lum

Bibliographic record

VenueJMIR Infodemiology · 2022
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
Fundersnot available
KeywordsModerationAnxietyContext (archaeology)Social mediaCross-sectional studyMental healthPsychologyPandemicMedicineGerontologyCoronavirus disease 2019 (COVID-19)Social psychologyPsychiatryDiseasePolitical scienceGeography

Abstract

fetched live from OpenAlex

Background Older adults were perceived as a vulnerable group during the COVID-19 pandemic due to the health and mental health challenges they faced. The pandemic was accompanied by an “infodemic” of overabundant and questionable information that has affected older adults’ mental health. As the infodemic and ageist narratives were prevalent online, more anxiety symptoms have been induced among older adults who used social media. Age-friendly communication, advocated by the World Health Organization’s Age-friendly City (AFC) guide, could be an antidote by providing tailored information via appropriate channels for older adults. Objective This study investigated the role of community capacity for age-friendly communication in mitigating anxiety during the pandemic. We hypothesized that age-friendly communication would moderate the effects of infection risks and social media use on anxiety. A double-moderating effect was hypothesized in the context of diminished trust in traditional media. Methods Data were collected from a cross-sectional telephone survey conducted in Hong Kong in 2020. Older adults (N=3421, age≥60 years) were interviewed about their well-being and daily lives. Community capacity for age-friendly communication was measured in a living district–based evaluation. It had 2 components: the reach of appropriate information to older adults (AFC-Information) and the age-friendliness of communication technologies (AFC-Communication Technology) in the community. We tested the hypothesized moderation and double-moderation effects with ordinary least squares regressions. Results Perceived COVID-19 infection risk (b=0.002, P=.02) and use of social media for COVID-19 information (b=0.08, P=.04) were associated with more anxiety symptoms. The effect of using social media was moderated by AFC-Information (b=–0.39, P=.002) and AFC-Communication Technology (b=–1.06, P<.001), and the effect of perceived COVID-19 infection risk was moderated by AFC-Information (b=–0.03, P=.002) and AFC-Communication Technology (b=–0.05, P<.001). Lower trust in traditional media exacerbated anxiety symptoms associated with social media use (b=–0.08, P=.02). Higher AFC-Information alleviated this moderation effect (AFC-Information × media trust b=–0.65, P<.001; AFC-Information × social media use b=–2.18, P<.001; 3-way interaction b=0.40, P=.003). Conclusions Our findings highlight the role of community age-friendly communication in mitigating anxiety related to the infodemic. Although using social media may have exacerbated the impact of the infodemic on older adults, it has the potential to deliver timely information for an adequate health response. Although the amplifying effects of low media trust was associated with social media use, age-friendly communication determined its strength. Instead of discouraging the use of digital technologies for COVID-19 information, efforts should be made in tailoring information and communication technologies in local communities for older adults.

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)0.011
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.100
GPT teacher head0.429
Teacher spread0.329 · 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 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

Citations6
Published2022
Admission routes1
Has abstractyes

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