MétaCan
Menu
Back to cohort
Record W2115111110 · doi:10.1089/cyber.2014.0549

Internet Use and Well-Being in Older Adults

2015· article· en· W2115111110 on OpenAlexaff
Jinmoo Heo, Sanghee Chun, Sunwoo Lee, Kyung Hee Lee, Junhyoung Kim

Bibliographic record

VenueCyberpsychology Behavior and Social Networking · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsBrock University
FundersUniverzita Palackého v Olomouci
KeywordsLonelinessThe InternetLife satisfactionPsychologySocial supportWell-beingExtant taxonContext (archaeology)GerontologyHealth and Retirement StudyClinical psychologySocial psychologyMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

The Internet has become an important social context in the lives of older adults. Extant research has focused on the use of the Internet and how it influences well-being. However, conflicting findings exist. The purpose of the study was to develop an integrative research model in order to determine the nature of the relationships among Internet use, loneliness, social support, life satisfaction, and psychological well-being. Specifically, loneliness and social support were tested as potential mediators that may modify the relationship between Internet use and indicators of well-being. Data from the U.S. Health and Retirement Study (HRS) were used, and the association among Internet use, social support, loneliness, life satisfaction, and psychological well-being was explored. The sample consisted of 5,203 older adults (aged 65 years and older). The results indicated that higher levels of Internet use were significant predictors of higher levels of social support, reduced loneliness, and better life satisfaction and psychological well-being among 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 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 score0.004
Threshold uncertainty score0.008

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.041
GPT teacher head0.318
Teacher spread0.277 · 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

Citations408
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCyberpsychology Behavior and Social NetworkingSame topicTechnology Use by Older AdultsFrench-language works237,207