The Brief Symptom Inventory-9 (BSI-9): Development and validation in a German general population sample
Bibliographic record
Abstract
Abstract Background The Brief Symptom Inventory-18 (BSI-18) is a self-report questionnaire with three subscales, somatisation, anxiety, and depression, based on longer measures of distress. The present study proposes a shorter, nine-item version (BSI-9) of the BSI-18 as a brief screening tool for distress. Methods Confirmatory factor analyses and reliability and validity analyses were carried out using a representative sample of the German general population. Confirmatory factor analysis demonstrates a good model fit for the three-dimensional BSI-9. Results The total scale was found to have strong internal consistency (αCronbach = 0.87 for the global severity index). The internal consistency coefficients of the three-item subscales reflect the brevity of these scales (somatisation αCronbach = 0.72, depression α Cronbach = 0.79, anxiety αCronbach = 0.68). The subscales were found to be significantly related with subscales of the Patient Health Questionnaire-4 and Hopkins Symptom Checklist-25. Limitations The present study used a limited number of distress measures, and a more recent dataset would be useful to provide a more current picture of the general population’s distress levels. Conclusions The BSI-9 provides a short, valid, and reliable screener for distress in the general population. Future work should examine its utility in clinical settings and different cultural contexts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".