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Record W2100954016 · doi:10.1177/1363461506066985

Mental Distress, Economic Hardship and Expectations of Life in Canada among Sudanese Newcomers

2006· article· en· W2100954016 on OpenAlexaffabout
Laura Simich, Hayley A. Hamilton, B. Khamisa Baya

Bibliographic record

VenueTranscultural Psychiatry · 2006
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsWorryMental healthRefugeeDistressImmigrationPsychologyDepression (economics)Settlement (finance)Mental distressPsychiatryMedicineGerontologyAnxietyClinical psychologyPolitical science

Abstract

fetched live from OpenAlex

As part of a settlement needs assessment of 220 recently arrived Sudanese refugees and immigrants in seven cities, we examined overall health status, indicators of mental distress, economic hardship and expectations of life in Canada. Data were collected in a community-based study using qualitative and quantitative techniques. Results indicate that those Sudanese for whom life in Canada was not what they expected and those who experienced economic hardship as measured by worry over having enough money for food or medicine experienced poorer overall health and reported a greater number of symptoms of psychological distress. After controlling for demographic and related variables, we found that individuals who were experiencing economic hardship were between 2.6 and 3.9 times as likely to experience loss of sleep, constant strain, unhappiness and depression, and bad memories as individuals who do not experience hardship. Healthcare providers should be aware of how postmigration social disadvantages may increase the risk of mental distress particularly among refugees.

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)0.001
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 score0.084
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.251
Teacher spread0.242 · 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

Citations139
Published2006
Admission routes2
Has abstractyes

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