Intergovernmental collaboration for the health and wellbeing of refugees settling in Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it