Evidence map›Paper›PMID 40632685›Full record

ArticlePloS one2025

Revealing the nuances of 'Grey Digital Divide' in Hong Kong: A latent profile analysis.

Bobo Hi-Po Lau, Eric Ngai-Yin Shum, Alex Pak-Ki Kwok, Ben Chi-Pun Liu, Alex Chi-Keung Chan, Rick Yiu-Cho Kwan, Steve Fu-Fai Fong, Gigi Lam, Chung-Kin Tsang, Daniel Dick-Man Leung and 4 more

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Bobo Hi-Po LauDepartment of Counselling & Psychology, Hong Kong Shue Yan University, Hong KongChina.ORCID https://orcid.org/0009-0002-3581-439X
Eric Ngai-Yin ShumDepartment of Counselling & Psychology, Hong Kong Shue Yan University, Hong KongChina.
Alex Pak-Ki KwokFaculty of Social Science, The Chinese University of Hong Kong, Hong KongChina.
Ben Chi-Pun LiuDepartment of Social Work, Hong Kong Shue Yan University, Hong KongChina.
Alex Chi-Keung ChanSchool of Arts and Humanities, Tung Wah College, Hong KongChina.ORCID https://orcid.org/0000-0003-3536-806X
Rick Yiu-Cho KwanSchool of Nursing, Tung Wah College, Hong KongChina.
Steve Fu-Fai FongDepartment of Social Work, Hong Kong Shue Yan University, Hong KongChina.ORCID https://orcid.org/0000-0001-7210-0092
Gigi LamDepartment of Sociology, Hong Kong Shue Yan University, Hong KongChina.
Chung-Kin TsangDepartment of Sociology, Hong Kong Shue Yan University, Hong KongChina.
Daniel Dick-Man LeungSchool of Nursing, Tung Wah College, Hong KongChina.
Johnson Chun-Sing CheungDepartment of Social Work & Social Administration, The University of Hong Kong, Hong KongChina.ORCID https://orcid.org/0000-0002-7837-282X
Jason Tak-Sang ChowDepartment of Counselling & Psychology, Hong Kong Shue Yan University, Hong KongChina.
Paulina Pui-Yun WongScience Unit, Lingnan University, Hong KongChina.
Stuart Gietel-BastenDivision of Social Science, Hong Kong University of Science & Technology, Hong KongChina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The 'grey digital divide' deprives older adults' equitable access to information and support, and thereby, their well-being. Policies including subsidies for internet access and devices, digital literacy classes, and telehealth support attempted to close the divide. Yet, it remains doubtful whether the discrepancy could be narrowed, or simply transformed. The mandatory COVID track-and-trace policy, the government's decade-long digital inclusivity initiatives and the city's high degree of digitization makes Hong Kong an exemplar for exploring the post-pandemic digital divide. Utilizing a person-centered approach, this study elaborated the intergenerational differences in digital engagement with a random sample of 870 younger (aged 18-54 years) and older (aged 55 years or above) adults (52.1% female) via phone interviews. With 16 indicators of digital motivation, access, digital skills, and usage, latent profile analysis (LPA) yielded three profiles - Proficient, Intermediate, and Novice, with disparate patterns between the younger (90.2%, 8.8%, 0.9%) and the older (59.2%, 35.5%, 5.2%) groups, demonstrating a clear intergenerational divide. Socio-economic status influenced profile membership regardless of age, and that profile membership relates to the frequencies of various social contacts except with family/relatives. Our findings demonstrate how typology defines the needs and assists formulation of segmented interventions toward digital inclusivity. (200 words).

Indexed as

COVID-19Digital DivideAdolescentAdultAgedFemaleHong KongHumansMaleMiddle AgedSocioeconomic FactorsTelemedicineYoung Adult

Identifiers

PMID40632685
PMCPMC12240337

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.