Intergovernmental collaboration for the health and wellbeing of refugees settling in Australia
Bibliographic record
Abstract
As outlined in the Department of Immigration and Border Protection Annual report 2016-17, Australia granted 21 928 humanitarian visas in 2016-17, 13 760 of them offshore. This number will increase in future to a planned offshore program of 18 750 in 2018-19. The report notes that the United Nations High Commissioner for Refugees ranks Australia third for the number of refugees resettled. With such a massive program and commitment by the Australian Government, the need to ensure that health and wellbeing are maintained or gained during the settlement process is paramount. This article outlines how collaboration between like-minded national governments can improve premigration health screening through information sharing, collaborative learning and increased capability in countries of origin to not only screen for illness and disability, but to more effectively put measures in place to address these before, during and after arrival. Australia, Canada, New Zealand, the UK and the US have worked together for more than a decade on migration health screening policies to ensure better management of health needs and successful resettlement. A case study about the Syrian refugee cohort, which began arriving in Australia in late 2015, illustrates how intergovernmental collaboration can improve settlement.
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".