The special psychiatric problems of refugees
Bibliographic record
Abstract
Abstract While the forced displacement of people from their homes has been described since ancient times, the past half-century has witnessed an expansion in the size of refugee populations of extraordinary numbers. In 1970, for example, there were only 2.5 million refugees receiving international protection, primarily through the United Nations High Commission for Refugees (UNHCR). By 2006, UNHCR was legally responsible for 8.4 million refugees. In addition, it is conservatively estimated that an additional 23.7 million people are displaced within the borders of their own countries. Although similar in characteristics to refugees who have crossed international borders, internally displaced persons do not receive the same protection of international law. Adding all refugee-type persons together, the world is forced to acknowledge the reality that over the past decade more than 10 000 people per day became refugees or internally displaced persons. The sheer magnitude of the global refugee crisis, the resettlement of large numbers of refugees in modern industrial nations such as Canada, the United States, Europe, and Australia, and the increased media attention to civil and ethnic conflict throughout the world has contributed to the medical and mental health issues of refugees becoming an issue of global concern. This chapter will focus on a comprehensive overview of the psychiatric evaluation and treatment of refugees and refugee communities. Although this mental health specialty is in its infancy, many scientific advances have been made that can facilitate the successful psychiatric care of refugee patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".