Exploring “Language of Suffering”: Idioms of Distress Among Eritrean Refugees Living in Israel
Bibliographic record
Abstract
In this paper, we explore idioms of distress among Eritrean refugees currently living in Israel, a refugee population that has experienced profound forms of violence and upheaval in their country of origin, yet largely overlooked in clinical research. A significant portion of Eritrean refugees have, over the past decade, sought asylum in Israel, and Eritrean refugees make up Israel’s largest refugee population. To explore their unique idioms of distress, data collection methods included 200 hours of participant observation. It also involved in-depth interviews with Eritrean refugees ( n = 26) and key informant interviews ( n = 9) with people of Eritrean descent, who were not only active in the Eritrean community but also engaged in service provision for community members. Findings uncovered four groups of idioms of distress for our Eritrean sample including mind-head-related idioms ( Bzuh mhasab , Ab aemroy selam ysen , Bzuh hasabat nab resey ymetseni , Hamam Resi ), distress-related idioms ( Chincket , Tsekti, and Tsulul ), trauma-related idioms ( Sineaemrawi Smbrat ), and supernatural-related idioms ( Buda and Tabib ). Our findings reveal that Eritrean refugees possess distinct idiomatic expressions rooted in broader cultural frameworks and systems of knowledge. These idioms reference a range of both pathological and non-pathological states, with meanings that may vary based on factors such as education, gender, duration of stay in the host country, and context of use. We argue that clinicians need to take the time to listen to refugees’ “language of suffering” and its cultural and contextual complexities in order to better understand their patients’ distress and provide more culturally appropriate and effective care.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".