Refugee health – collaborating for better outcomes
Bibliographic record
Abstract
Refugee health is a topical and important issue.Although psychological issues are well described, and refugees from certain regions are at risk of a range of infectious diseases, many people from refugee backgrounds also experience chronic physical diseases and/or live with a disability. 1 Their health status has evolved in the context of organised violence marked by persecution, forced exile from their homelands, and grief and loss at many levels.Resettlement in a new country has its own challenges, often prolonged. 2here are some differences between the needs of asylum seekers and those of refugees who enter Australia as part of its humanitarian migration program.However, asylum seekers and refugees share many common concerns, especially long-term conditions such as psychological distress associated with their experiences and the uncertainty of life in Australia. 3Ngo and colleagues explore the detection rates for health conditions screened after arrival in Australia, and the importance of tailoring screening to refugees' migration history and risk.This is especially important for refugees from Middle Eastern countries such as Syria, whose risk profile is different to those from traditional refugee source countries.Despite the politics in Australia, there is genuine goodwill and concern to ensure that people from refugee backgrounds get the care and support they need.However, realising this objective is challenging.One major reason is the fragmentation that can occur between specialised refugee services and mainstream, public and nongovernment health and welfare services.Over time, this can create discontinuities that may lead to refugees' health and social needs being overlooked and neglected.Because of their lower health literacy, refugees are especially vulnerable to these gaps.Key requirements for better integrated care include good professional relationships between providers, effective communication and sharing of information, and clear and supported pathways between services.This issue of Public Health Research & Practice contains examples of where this integration works at the international level.Martin and Douglas describe the international cooperation involved in premigration screening, especially between the US, UK, Australia, Canada and New Zealand.Pottie and colleagues illustrate the high level of collaboration between government, nongovernment, private and professional organisations in Canada in providing primary care for refugees.
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.
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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.096 | 0.014 |
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".