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Record W1625350991 · doi:10.15353/joci.v8i1.3059

Life-Based Design Against Loneliness Among Older People

2012· article· en· W1625350991 on OpenAlexvenueno aff
Jaana Leikas, Pertti Saariluoma, Rebekah Rousi, Erkki Kuisma, Hannu Vilpponen

Bibliographic record

VenueThe Journal of Community Informatics · 2012
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessFeelingEveryday lifeContext (archaeology)PsychologySocial lifeSocial psychologySociologySocial scienceEpistemology

Abstract

fetched live from OpenAlex

Developing successful service concepts has to rely on the efficient exploitation of knowledge about everyday life. This design thinking is called life-based design (LBD) and it is based on examining different aspects of forms of life as a starting point for technology design. Here, we consider LBD in the context of reducing the feeling of loneliness. Via the implementation of focus groups, we studied how different age groups experienced loneliness, and examined whether generational formation could give insight into analysing different life situations and forms of life in the context of social relationships. Exploring forms of life is a focal tool of LBD, stressing the importance of understanding the cultural, social and psychological facts and values that would explain people’s actions in a particular life situation. Finally, we suggest an integrated service system to decrease the feeling of loneliness.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.597
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0020.000
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 head0.049
GPT teacher head0.288
Teacher spread0.239 · 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 designQualitative
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

Citations9
Published2012
Admission routes1
Has abstractyes

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