MétaCan
Menu
Back to cohort
Record W2919655134 · doi:10.1177/0956797619836102

Loneliness and Neighborhood Characteristics: A Multi-Informant, Nationally Representative Study of Young Adults

2019· article· en· W2919655134 on OpenAlexfundno aff
Timothy Matthews, Candice L. Odgers, Andrea Danese, Helen L. Fisher, Joanne B. Newbury, Avshalom Caspi, Terrie E. Moffitt, Louise Arseneault

Bibliographic record

VenuePsychological Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNuffield FoundationEconomic and Social Research CouncilCanadian Institute for Advanced ResearchMedical Research CouncilNational Institute on AgingJacobs Foundation
KeywordsLonelinessPsychologyFeelingPerceptionDevelopmental psychologyClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

In this study, we investigated associations between the characteristics of the neighborhoods in which young adults live and their feelings of loneliness, using data from different sources. Participants were drawn from the Environmental Risk Longitudinal Twin Study. Loneliness was measured via self-reports at ages 12 and 18 years and also by interviewer ratings at age 18. Neighborhood characteristics were assessed between the ages of 12 and 18 via government data, systematic social observations, a resident survey, and participants' self-reports. Greater loneliness was associated with perceptions of lower collective efficacy and greater neighborhood disorder but not with more objective measures of neighborhood characteristics. Lonelier individuals perceived the collective efficacy of their neighborhoods to be lower than did their less lonely siblings who lived at the same address. These findings suggest that feelings of loneliness are associated with negatively biased perceptions of neighborhood characteristics, which may have implications for lonely individuals' likelihood of escaping 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.425
Teacher spread0.378 · 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 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

Citations64
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePsychological ScienceSame topicHealth disparities and outcomesFrench-language works237,207