Health of children in Australian immigration detention centres: An analysis of the quarterly health reports from 2014 to 2017
Bibliographic record
Abstract
AIM: This study examines 3 years of child and adolescent health data from Australian onshore and offshore immigration detention centres from 2014 to 2017, quantifying the health presentation data of children and adolescents in Australian immigration detention and comparing rates between onshore and offshore detention. METHODS: This study utilised the Quarterly Immigration Detention Health Reports over a period of 3 years. To compare onshore and offshore datasets, we calculated the rate of health events per quarter against the estimated quarterly onshore and offshore detention population of children. We 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. RESULTS: The estimated number of children detained per quarter onshore ranged from 700 in 2014 (quarter 3) to 13 in 2016 (quarters 3 and 4); the estimated quarterly population of children in offshore detention ranged from 186 in 2014 (quarter 3) to 42 in 2017 (quarter 2). Children offshore had significantly higher rates of consultations with a mental health nurse (z = -1.96; P = 0.002), psychologist (z = -2.32; P = 0.01) and counsellor (z = -3.41; P < 0.001). As for reasons for presentation to general practitioners and psychiatrists, complaints related to skin (z = -1.97; P = 0.05), respiratory issues (z = -1.96; P = 0.05) and urological issues (z = -2.21; P = 0.03) were significantly higher amongst children detained offshore. CONCLUSIONS: Compared to children in the Australian community, children detained both onshore and offshore had greater health needs. Children offshore also presented more frequently with a range of complaints and accessed health services at higher rates than children detained onshore; this adds to growing evidence about the harms of offshore detention and detention more generally.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".