Räumlich-zeitliche Psychopathologie – deutsche Version der Scale for Space and Time Experience in Psychosis (STEP)
Bibliographic record
Abstract
Historical authors (e.g., Ludwig Binswanger and Eugène Minkowski) postulated that the experience of patients with schizophrenia is characterized by time fragmentation. From a clinical perspective, patients with schizophrenia also suffer from difficulties in spatial perception (e.g., abnormalities in the experience of interpersonal distance and spatial orientation). Although these changes can lead to a serious detachment from reality, to considerable suffering of the affected persons and to difficulties in the therapeutic process, the abnormal experience of space and time in psychotic disorders has not yet been sufficiently investigated. One possible reason is the lack of appropriate and standardized instruments that quantify the experience of space and time in patients with psychotic disorders. Based on an innovative concept, the so-called spatiotemporal psychopathology (STPP), a clinical rating scale for the systematic-quantitative assessment of spatial and temporal experience in patients with psychotic disorders was developed. This article presents the German version of the Scale for Space and Time Experience in Psychosis (STEP). The original English version of the STEP measures different spatial (14 phenomena) and temporal (11 phenomena) phenomena in 25 items. The STEP shows both a high internal consistency (Cronbach's alpha = 0.94) and a significant correlation with the Positive and Negative Syndrome Scale (PANSS; p < 0.001). In summary, the German version of the STEP scale presented here represents an important instrument in the German-speaking countries for the assessment of spatial and temporal experience in patients with psychotic disorders.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".