Health and health care in Australian immigration detention: a comparison between onshore and offshore data
Bibliographic record
Abstract
Purpose This study aims to compare the impact of Australian onshore and offshore immigration detention centres (IDCs) on detainees’ health and health-care events. Design/methodology/approach It uses data extracted from the Australian Government’s quarterly health reports from 2014 to 2017. These reports contain a range of data about the health and well-being of detainees, including complaints/presenting symptoms and number of appointments and hospitalisations. To compare onshore and offshore data sets, the authors calculated the rate of health events per quarter against the estimated quarterly onshore and offshore detention population. They ran a series of two-proportion z-tests for each matched quarter to calculate median z- and p-values for all quarters. These were used as an indicator as to whether the observed differences between onshore and offshore events were statistically significant. Findings The results suggest that adults detained onshore and offshore have substantial health needs, however, almost all rates were far higher in offshore detention, with people more likely to raise a health-related complaint, access health services and be prescribed medications, often at two to three times the rate of those onshore. Originality/value This paper adds to a modest body of literature that explains the health of people detained in Australian IDCs. To the best of the authors’ knowledge, this is the first paper to explore health service utilisation and a range of other variables found in the Australian Government’s quarterly health reports. These findings bolster the evidence which suggests that detention, and particularly offshore detention is particularly harmful to health.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".