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Record W2987487289 · doi:10.1093/geroni/igz038.1980

DEPRESSION AMONG OLDER CANADIAN REFUGEES: THE PROTECTIVE ROLE OF SOCIAL SUPPORT

2019· article· en· W2987487289 on OpenAlexaffabout
Esme Fuller‐Thomson, Shen Lin, Karen Kobayashi, Simran R. A. Arora, Hongmei Tong, Karen Davison

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsKwantlen Polytechnic UniversityMacEwan UniversityUniversity of VictoriaUniversity of Toronto
FundersDirectorate for Biological Sciences
KeywordsRefugeeOddsDepression (economics)MedicineOdds ratioPsychological interventionSocial supportVulnerability (computing)DemographySocial isolationPsychiatryGerontologyPsychologyLogistic regressionInternal medicinePolitical scienceSociologySocial psychology

Abstract

fetched live from OpenAlex

Abstract This study’s objective was to identify which factors attenuate refugees’ higher odds of depression. A secondary analysis of 272 refugees and 29,398 non-refugees in the Canadian Longitudinal Study on Aging, a 2012 study of Canadians aged 45 to 85, was conducted. The prevalence of depression was higher among refugees than non-refugees (22.1% vs 15.2%, p<.001). The age-sex adjusted odds of depression for refugees (OR=1.70, p<.001) was only modestly attenuated when sociodemographic characteristics, physical health conditions, chronic pain, binge drinking and level of physical activity were taken into account (ORs ranged from 1.61 to 1.70, all p<.05). However, in the model adjusting for social support, the odds of depression for refugees was reduced to non-significance (OR=1.30, p=0.92). Refugees have higher odds of depression than non-refugees, and this excess vulnerability is associated with lower levels of social support. Targeted interventions to decrease isolation and improve refugees’ social support warrant greater attention

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.074
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.318
Teacher spread0.306 · 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 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

Citations0
Published2019
Admission routes2
Has abstractyes

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