The impact of immigration detention on the mental health of refugees and asylum seekers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".