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Record W4381469826 · doi:10.1002/jts.22944

The impact of immigration detention on the mental health of refugees and asylum seekers

2023· article· en· W4381469826 on OpenAlexaff
Walter Forrest, Zachary Steel

Bibliographic record

VenueJournal of Traumatic Stress · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsRichmond Hospital
Fundersnot available
KeywordsRefugeeImmigration detentionMental healthDistressPsychiatryImmigrationAsylum seekerOddsMedicineOdds ratioDemographyPsychologyClinical psychologyLogistic regressionPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

Studies consistently report that asylum seekers held in immigration detention have relatively high rates of mental distress, yet evidence of the long-term impact of immigration detention is limited. Using propensity score-based methods, we estimated the impact of immigration detention on the prevalence of nonspecific psychological distress, using the Kessler-6, and probable posttraumatic stress disorder (PTSD), using the PTSD-8, among participants in a national sample of asylum seekers in the 5 years following their resettlement in Australia (N = 334). At Wave 1, the prevalence of nonspecific psychological distress was high among all participants regardless of detainment status, OR = 0.28, 95% CI [0.04, 2.06], and did not change over time for either detainees (n = 222), OR = 1.01, 95% CI [0.46, 2.18], or nondetainees (n = 103), OR = 0.81, 95% CI [0.39, 1.67]. In contrast, the odds of probable PTSD were significantly higher for former detainees, OR = 8.20; 95% CI [2.61, 26.73], than nondetainees at Wave 1; although they declined among former detainees, OR = 0.56, 95% CI [0.38, 0.82]), and increased among nondetainees, OR = 1.57, 95% CI [1.11, 2.23], in the years following resettlement. These results imply the use of immigration detention to manage unauthorized migration increases the prevalence of probable PTSD in the short term among former detainees who have resettled in Australia.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.031
GPT teacher head0.380
Teacher spread0.350 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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