Somatosensory Amplification Scale—Chinese version: psychometric properties and its mediating role in the relationship between alexithymia and somatization
Bibliographic record
Abstract
The Somatosensory Amplification Scale (SSAS) was designed to measure individual's tendency to experience visceral and somatic sensations as unusually intense, disturbing and alarming. In this study, we aimed to investigate the reliability and validity of the SSAS in the Chinese general population, as well as the mediating effect of somatosensory amplification in the relationship between alexithymia and somatization. A total of 386 healthy adults were enrolled in this study. Participants completed the Chinese versions of the Somatosensory Amplification Scale (SSAS-C), the somatization subscale of the Symptom Check List 90 (SCL-90 som), the Toronto Alexithymia Scale (TAS-20), and the Short form Health Anxiety Inventory (SHAI). One hundred and thirty-three participants were randomly selected to complete the SSAS-C again two weeks after the initial assessment. The reliability and validity of the SSAS-C were analyzed. Confirmatory factor analysis showed that the one-factor model achieved adequate model fits; one item was deleted due to low factor loading. The revised SSAS-C showed good internal consistency and test-retest reliability. The SSAS-C scores correlated positively with the scores of SCL-90 som, TAS-20 and the SHAI, showing good convergent validity. In addition, somatosensory amplification mediated the association between alexithymia and somatization. The Chinese version of SSAS has acceptable reliability and validity for the general population. In addition, alexithymia may increase somatization through higher somatosensory amplification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".