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Determinants of Depression Among Ethiopian Immigrants and Refugees in Toronto

2004· article· en· W1980641340 on OpenAlexaffabout
Haile Mekonnen Fenta, Ilene Hyman, Samuel Noh

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2004
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreCentre for Addiction and Mental Health
Fundersnot available
KeywordsRefugeeImmigrationDepression (economics)Mental healthMedicineDemographyPopulationPsychiatryIntervention (counseling)GerontologyEnvironmental healthGeographySociology

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the occurrence of and risk factors for depressive disorder in a random sample of 342 Ethiopian immigrants and refugees in Toronto. The Composite International Diagnostic Interview questionnaire was used to measure depression. The results suggested a lifetime prevalence of depression among Ethiopian immigrants and refugees of 9.8%, which was slightly higher than the lifetime prevalence rate in the Ontario population (7.3%). However, the rate among Ethiopian immigrants and refugees was approximately three times higher than the rate estimated for Southern Ethiopia (3.2%). The data confirmed the significance of known risk factors for depression in immigrants, including younger age, experiences of premigration trauma, refugee camp internment, and postmigration stressful events. The implication of the overall finding is that there is a need to develop mental health intervention programs, particularly for people who have experienced premigration trauma, refugee camp internment, and postmigration stresses.

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)0.000
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 score0.593
Threshold uncertainty score0.810

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.329
Teacher spread0.318 · 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

Citations216
Published2004
Admission routes2
Has abstractyes

Explore more

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