Resettling refugees and safeguarding their mental health: Lessons learned from the Canadian Refugee Resettlement Project. Transcult Psychiatry
Bibliographic record
Abstract
between 1979 and 1981, is one of the largest, most comprehensive and longest-lived investigations of refugee resettlement ever carried out. Knowl-edge gleaned from the RRP about research methodology, about the resettle-ment experience, about the social costs of resettling refugees, about factors that promote or hinder integration, about risk and protective factors for refugee mental health, and about the refugees ’ consumption of mental health and social services is summarized in the form of 18 “Lessons. ” The lessons are offered in order to encourage and stimulate further research, as well to suggest policy and practice innovations that could help make resettlement easier, less costly, more effective, and more humane. Key words integration • mental health • refugee • resettlement • Southeast Asian Canada is one of 147 United Nations member states to have signed the UN Convention on refugees, joining in an international commitment to provide asylum for the persecuted and stateless. Canada is also one of a much smaller group of Convention signatories – about 20 – who offer not just temporary protection, but the option of permanent resettlement. Vol 46(4): 539–583 DOI: 10.1177/1363461509351373 www.sagepublications.com Copyright © 2009 McGill University transcultural psychiatry
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".