Efficient identification of mental health problems in refugees in Germany: the Refugee Health Screener
Bibliographic record
Abstract
<b>Background:</b> A substantial number of refugees present with mental disorders. This appears particularly acute in the currently increasing refugee populations in Europe. Although EU guidelines demand the identification and support of vulnerable individuals such as survivors of trauma, no adequately validated and comprehensive mental health screening instruments for refugees residing in Europe currently exist. <b>Objective:</b> We studied the feasibility, validity, and reliability of the Refugee Health Screener-15 (RHS-15) – a time-efficient and easy-to-implement screening developed by Hollifield et al. (2013) – as a self-rating and interview instrument. <b>Methods:</b> A sample of refugees from different countries (<i>N</i> = 86), representative of those who had arrived around the turn of the year 2015/2016 in Germany, filled in the RHS-15 on their own. A semi-structured clinical interview was later conducted with a random subsample (<i>n</i> = 56). <b>Results:</b> Fifty-two percent of the refugees examined screened positive in the RHS-15, thus indicating current mental health problems. The RHS-15 showed a good feasibility, reliability, and validity in both the self-rating and the interview version. It detected clinically relevant mental health problems when PTSD, depression, anxiety, or somatization problems were present. A shorter 13-item version proved to be equally valid. <b>Conclusions:</b> Together with previous research on the RHS in refugees living in the US, this suggests that the RHS is a time-efficient and accurate instrument that is able to detect common mental health problems in a wide range of refugees. Prospectively, the RHS could be used as an instrument for identifying vulnerable refugees, for example, by integrating it in the initial medical examination in the host community, thereby initiating support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".