Cross-culturally adapting the GHQ-12 for use with refugee populations: Opportunities, dilemmas, and challenges
Bibliographic record
Abstract
This article discusses the opportunities, dilemmas, and challenges involved in the cross-cultural adaptation (CCA) of psychological scales for use with refugee populations. It draws on insights derived from an attempt to adapt the 12-item General Health Questionnaire (GHQ-12) to the particular culture and context of Eritrean refugees residing in Israel. Multiple techniques including expert translations, a focus-group discussion, a survey, and piloting, were employed to attain a cross-cultural and conceptually equivalent measure. During the CCA process, the research team encountered issues pertaining to conceptual non-equivalence, the structure of the measure's responses and scoring system, and acceptability. These issues required the team to move beyond semantic translation by adapting certain items. This study demonstrates the compromises which need to be made in the adaptation process and indicates the potential bias which each of these compromises introduces. Despite its limitations, CCA does appear to significantly improve detection of mental health symptoms in refugee populations. Overall, the results of the present study provide support for the notion that the sensitive and appropriate assessment of individuals from refugee backgrounds requires adopting a rigorous, systematic, and contextual approach to instrument adaptation, with an emphasis on the integration of idioms of distress as well as the adaptation of Likert-type scales.
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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 | 0.173 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".