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Record W4411120367 · doi:10.1111/glob.70015

The Well‐Being and Ill‐Being of Transnational Grandparents of Migrant Families Living in Canada

2025· article· en· W4411120367 on OpenAlexafffundabout
Lisa Merry, Mònica Ruíz Casares, Isabelle Archambault, Jill Hanley

Bibliographic record

VenueGlobal Networks · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsMcGill UniversityToronto Metropolitan UniversityUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGrandparentClosenessFeelingHappinessWell-beingPsychologySocial psychologySociologyDevelopmental psychology

Abstract

fetched live from OpenAlex

ABSTRACT Using a comparative approach, we deconstruct and examine the different facets of well‐being and ill‐being of transnational grandparents whose migrant adult children and grandchildren are living in Canada. Experiences ranged from happiness and satisfaction to grandparents being severely affected and deeply suffering. Well‐being and ill‐being were shaped by the following: (1) grandparents’ perspectives and feelings about the transnational circumstances, how they processed and framed the situation; (2) their relationships with migrant family members, including the quality of interactions, communication and ability to preserve closeness; (3) how they experienced the support role they play in their families’ lives and (4) family dynamics regarding the direction and level of dependence for support within the family. The socio‐cultural, economic and migration contexts affected grandparents’ expectations, perspectives and their sense of control and capacity to give and stay connected with their migrant families. The findings underscore the interconnectedness of family members’ well‐being and ill‐being across borders.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.003
GPT teacher head0.224
Teacher spread0.221 · 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 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 routes3
Has abstractyes

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