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Record W4407003249 · doi:10.1177/21568693241310875

The Co-Evolution of Personal Networks and Loneliness Following Widowhood: Resources or Costs for Older Men and Women?

2025· article· en· W4407003249 on OpenAlexaff
Jina Lee, Markus H. Schafer

Bibliographic record

VenueSociety and Mental Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLonelinessPsychologyGerontologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Drawing from the life-course framework and an integrated model of social resources and social costs, this article investigates (1) the dynamics of personal network changes, (2) their impact on loneliness following widowhood, and (3) the gender-specific effects of these changes. Analyzing panel data from the German Ageing Survey ( N = 7,012; observations = 20,816) using multi-level mixed-effects models, the study reveals a modest expansion in non-kin networks and the number of children in networks after widowhood. Additionally, the findings indicate that over time, widowed individuals generally experience a reduction in the distance to their nearest network members, particularly kin. Growing non-kin networks are associated with lower loneliness following widowhood. Geographic changes in networks display gender-specific patterns: proximity to children is linked to reduced loneliness for widowed men, but greater loneliness for widowed women. These results underscore the complex and gendered nature of relational adaptations to widowhood, highlighting that network changes can offer both benefits and challenges during life transitions. The study also suggests that considering opportunity costs can be a valuable extension of the social cost framework.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.007
GPT teacher head0.315
Teacher spread0.307 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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