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Record W4412364797 · doi:10.1177/01640275251357848

Loneliness, Family Devotion, Care Provision, and Gendered Façades Among Black Older Adults in Canada: A Narrative Qualitative Study

2025· article· en· W4412364797 on OpenAlexaffabout
Blessing Ugochi Ojembe, Oluwagbemiga Oyinlola, Michael Kalu, Emmanuel Eugene, Ann Aninma

Bibliographic record

VenueResearch on Aging · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsYork UniversityMcGill UniversityUniversity of Manitoba
Fundersnot available
KeywordsLonelinessNarrativeCollectivismNarrative inquiryPsychologyPsychological interventionQualitative researchDevelopmental psychologyGender studiesSocial psychologySociologyIndividualismPolitical science

Abstract

fetched live from OpenAlex

Despite growing awareness of loneliness and social isolation among older adults, efforts to address these issues among Black older adults (BOAs) in Canada remain limited. Literature subsumes their lived experiences within broader racialized populations, obscuring their unique challenges. This study explores the narratives of 13 BOAs ( n = 5 male, n = 8 female, Mean Age: 68.3) on their roles as both family caregivers and care recipients, examining how these dynamics shape their experiences of loneliness. Using a collectivist theoretical lens and a narrative inquiry approach, the analysis identified three key themes: (1) care and support from adult children, (2) family devotion, and (3) the tension between gendered façades and nurturing dynamics. While cultural expectations of family devotion and caregiving reinforced a strong sense of duty, they often masked deeper emotional gaps. These findings signal the need for culturally tailored interventions that address the emotional landscapes of BOAs, moving beyond generalized assumptions.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.120
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0240.008
Scholarly communication0.0040.002
Open science0.0020.005
Research integrity0.0010.002
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 head0.092
GPT teacher head0.492
Teacher spread0.401 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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