Inter‐rater reliability of the German version of the Nurses' Global Assessment of Suicide Risk scale
Bibliographic record
Abstract
In comparison to the general population, the suicide rates of psychiatric inpatient populations in Germany and Switzerland are very high. An important preventive contribution to the lowering of the suicide rates in mental health care is to ensure that the risk of suicide of psychiatric inpatients is assessed as accurately as possible. While risk-assessment instruments can serve an important function in determining such risk, very few have been translated to German. Therefore, in the present study, we reported on the German version of Nurses' Global Assessment of Suicide Risk (NGASR) scale. After translating the original instrument into German and pretesting the German version, we tested the inter-rater reliability of the instrument. Twelve video case studies were evaluated by 13 raters with the NGASR scale in a 'laboratory' trial. In each case, the observer's agreement was calculated for the single items, the overall scale, the risk levels, and the sum scores. The statistical data analysis was conducted with kappa and AC1 statistics for dichotomous (items, scale) scales. A high-to-very high observers' agreement (AC1: 0.62-1.00, kappa: 0.00-1.00) was determined for 16 items of the German version of the NGASR scale. We conclude that the German version of the NGASR scale is a reliable instrument for evaluating risk factors for suicide. A reliable application in the clinical practise appears to be enhanced by training in the use of the instrument and the right implementation instructions.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".