Scale for time and space experience in anxiety (STEA): Phenomenology and its clinical relevance
Bibliographic record
Abstract
Anxiety is a pervasive emotional state where, phenomenologically, subjects often report changes in their experience of time and space. However, a systematic and quantified examination of time and space experience in terms of a self-report scale is still missing which eventually could also be used for clinical differential diagnosis. Based on historical phenomenological literature and patients' subjective reports, we here introduce, in a first step, the Scale for Time and Space Experience of Anxiety (STEA) in a smaller sample of 19 subjects with anxiety disorders and, in a second step, validate its shorter clinical version (cSTEA) in a larger sample of 48 anxiety subjects. The main findings are (i) high convergent and divergent validity of STEA with both Beck Anxiety Inventory (BAI) (r = 0.7325; p < 0.001) and Beck Depression Inventory (BDI) (r = 0.7749; p < 0.0001), as well as with spontaneous mind wandering (MWS) (r = 0.7343; p < 0.001) and deliberate mind wandering (MWD) (r = 0.1152; p > 0.05), (ii) statistical feature selection shows 8 key items for future clinical usage (cSTEA) focusing on the experience of temporal and spatial constriction, (iii) the effects of time and space experience (i.e., for both STEA and cSTEA scores) on the level of anxiety (BAI) are mediated by the degree of spontaneous mind wandering (MWS), (iv) cSTEA allows for differentiating high levels of anxiety from the severity of comorbid depressive symptoms, and (v) significant reduction in the cSTEA scores after a therapeutic intervention (breathing therapy). Together, our study introduces a novel fully quantified and highly valid self-report instrument, the STEA, for measuring time-space experiences in anxiety. Further we develop a shorter clinical version (cSTEA) which allows assessing time space experience in a valid, quick, and simple way for diagnosis, differential diagnosis, and therapeutic monitoring of anxiety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".