Screening for Major Depression and Posttraumatic Stress Disorder among Asylum Seekers: Adapting a Standardized Instrument to the Social and Cultural Context
Bibliographic record
Abstract
OBJECTIVE: To adapt the PTSD and MDE sections of a validated psychiatric diagnostic instrument, we used the Mini International Neuropsychiatric Interview (MINI) during an initial health assessment into a primary care facility for asylum seekers. METHOD: A 3-step process was carried out. First, items of the original version of the MINI were adapted to the specific context of life of asylum seekers in the host country (by a multidisciplinary group that included public health nurses, a primary care physician, a psychologist, a psychiatrist, and an epidemiologist). Second, we submitted the reworded and original versions of the MINI to 14 interpreters' who tested for general and cultural acceptability. Each diagnostic criterion was rated according to interpreters' comments on a 4-point Likert scale (1 = an item good for translation and 4 = an unusable or completely inadequate item). In the third step, we rephrased the most problematic items identified by the interpreters. RESULTS: Some original items were considered particularly ill-adapted for this context, and 4 had to be dropped. This final rewording took into account cultural inadequacies and lack of structure (including temporal organization) of the everyday life of newly arrived asylum seekers. CONCLUSION: The reworded MINI was successfully tested, and its items are presented in the final part of the study.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".