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Record W4312103552 · doi:10.1093/geroni/igac059.1172

MEASURING OLDER ADULT LONELINESS ACROSS COUNTRIES

2022· article· en· W4312103552 on OpenAlexaff
Lauren Newmyer, Ashton M. Verdery, Rachel Margolis, Léa Pessin

Bibliographic record

VenueInnovation in Aging · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsWestern University
Fundersnot available
KeywordsLonelinessPsychologySpouseScale (ratio)Construct (python library)UCLA Loneliness ScaleDeveloping countryConstruct validityDevelopmental psychologySocial psychologyGeographyPsychometricsPolitical scienceComputer scienceEconomics

Abstract

fetched live from OpenAlex

Abstract The topic of older adult loneliness commands increasing media and policy attention around the world. Are surveys of aging equipped to measure it? We assess the measurement of loneliness in large-scale aging studies in 31 countries. In each country, we document available loneliness measures, examine correlations between different measures, and assess how these correlations differ by gender and age group. There is substantial heterogeneity in available measures of loneliness across countries. Within countries with multiple measures, the correlations between measures are high (range .38-.78). Differences by age and gender group are relatively small. Correlations between loneliness measures and living alone and being without a spouse are positive and similar in magnitude across countries, supporting construct validity. We establish that even single-item measures of loneliness contribute meaningful information in diverse contexts, with reliable and consistent measurement properties within many countries.

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)0.007
metaresearch head score (Gemma)0.019
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0000.000
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 head0.057
GPT teacher head0.372
Teacher spread0.315 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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