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Record W1576580366

SOCIAL AND EMOTIONAL IMPACTS OF INTERNET USE ON OLDER ADULTS

2015· article· en· W1576580366 on OpenAlexaffabout
Fan Zhang, David Kaufman

Bibliographic record

VenueEuropean Scientific Journal ESJ · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLonelinessBelongingnessPsychologySocial engagementSocial capitalSocial isolationGerontologyThe InternetMental healthSocial supportFeelingSocial network (sociolinguistics)UCLA Loneliness ScaleMultilevel modelSocial mediaSocial psychologyDevelopmental psychologySociologyMedicinePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

The Internet has become a means by which older adults can maintain offline relationships with family and friends, and develop new social networks. Social engagement plays a important role in later life. Staying socially active can help older adults maintain physical and cognitive health. Social capital is also important for older adults‘ mental health and wellbeing. This study examined whether older adults’ online social activities are associated with some social and emotional factors. A total of 82 participants were recruited from two community seniors’ centres in Canada. The results of a series of hierarchical regression analyses indicated that older adults’ online social activities were positively related to bridging social capital, belongingness and self-esteem, and negatively associated with the feeling of loneliness. The result of a canonical correlation analysis revealed that meeting new people online and great amount of Internet use is predictive of online bridging social capital.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.312
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 source (direct Gemma or distilled Codex), 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

Citations18
Published2015
Admission routes2
Has abstractyes

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Same venueEuropean Scientific Journal ESJSame topicTechnology Use by Older AdultsFrench-language works237,207