Psychologists’ Perspectives on the Psychological Suffering of Refugee Patients in Brazil
Bibliographic record
Abstract
Worldwide there are 79.5 million displaced people, many of which face war, violence, tragic flights and struggles in host countries. Research shows augmented prevalence rates of mental disorders among refugees internationally, but little is known about refugee mental health in Latin American countries. Furthermore, only a few studies have taken into consideration the knowledge of clinical psychologists who treat refugee patients. The present study examines the experiences of 32 psychologists in Brazil regarding their refugee patients' psychological suffering and mental disorders. Semi-structured interviews were conducted in various locations in Brazil and analysed following a consensual qualitative research approach. Four clusters of refugee patients' suffering were synthesised: post-migration stressors, traumatic experiences, flight as life rupture, and the current situation in the country of origin. The most frequently described conditions in patients were anxiety and depression. However, the results also show that the use of manuals for the classification of mental disorders is contested among psychologists in Brazil. Most psychologists stressed patients' socio-political suffering and saw patients' symptoms as normal reactions to their experiences. There is a need to acknowledge the socio-political suffering of refugees in Brazil and foster their mental health by tackling current post-migration stressors such as discrimination.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".