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Immigration, lack of control and psychological distress: Findings from the Oslo Health Study

2006· article· en· W2123797554 on OpenAlexaff
Odd Steffen Dalgard, Suraj Bahadur Thapa, Edvard Hauff, Michael McCubbin, Hammad Raza Syed

Bibliographic record

VenueScandinavian Journal of Psychology · 2006
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNorwegianPsychosocialSocioeconomic statusPsychologyImmigrationSocial supportDistressChecklistClinical psychologyPsychological distressMental healthPsychiatrySocial psychologyMedicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

The aims of this study are to compare the level of psychological distress between Norwegian born and immigrants from countries with different income levels and culture, and to investigate the explanatory effect of socioeconomic and psychosocial factors, with special emphasis on lack of control (powerlessness and self-efficacy). A cross-sectional survey with self-administered questionnaire was conducted in 2000-2001 in a sample of 15,723 adults living in Oslo. Psychological distress was measured by a ten-item shortened version of Hopkins Symptom Checklist-25 items, whereas psychosocial variables were measured by various instruments. The results show that the level of psychological distress is significantly higher in immigrants from low- and middle-income countries than in the Norwegian born and the immigrants from high-income countries. They also report more powerlessness, more negative life events, less social support, less income and less paid work. It is concluded that negative life events, mainly related to social network, somatic health and economic situations, as well as lack of social support, are important mediators between immigration from low- and middle-income countries to Norway and psychological distress. Powerlessness also plays a role, but this is mainly because of a concept overlap between psychological distress and powerlessness.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.068
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.046
GPT teacher head0.396
Teacher spread0.351 · 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.

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

Citations109
Published2006
Admission routes1
Has abstractyes

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