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Record W2773691260 · doi:10.1111/nin.12226

Social dimensions of health across the life course: Narratives of Arab immigrant women ageing in Canada

2017· article· en· W2773691260 on OpenAlexaffabout
Jordana Salma, Norah Keating, Linda Ogilvie, Kathleen F. Hunter

Bibliographic record

VenueNursing Inquiry · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLonelinessLife course approachSocial supportImmigrationSocial connectednessPopulation ageingGender studiesGerontologyQualitative researchSociologyTransnationalismPsychologyPopulationMedicinePolitical scienceSocial psychologySocial sciencePolitics

Abstract

fetched live from OpenAlex

The increase in ethnically and linguistically diverse older adults in Canada necessitates attention to their experiences and needs for healthy ageing. Arab immigrant women often report challenges in maintaining health, but little is known about their ageing experiences. This interpretive descriptive study uses a transnational life course framework to understand Arab Muslim immigrant women's experiences of engaging in health-promoting practices as they age in Canada. Women's stories highlight social dimensions of health such social connectedness, social roles and social support that are constructed and maintained within different migration contexts across the life course. Barriers and facilitators to healthy ageing in this population centred around five themes: (i) the necessity of staying strong, (ii) caring for self while caring for others, (iii) double jeopardy of chronic illnesses and loneliness, (iv) inadequate support within large social networks and (v) navigating access to health-supporting resources. The findings point to transnational connections and post-migration social support as major influencers in creating facilitators and barriers to healthy ageing for Arab Muslim immigrant women.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.074
GPT teacher head0.422
Teacher spread0.348 · 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

Citations30
Published2017
Admission routes2
Has abstractyes

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