1190 – Assessing Alexithymia In Different Subgroups Of The Chinese Population: Sample Invariance Of The Chinese Version Of Toronto Alexithymia Scale-short Form (tas-9-c)
Bibliographic record
Abstract
Introduction The TAS-9-C is a short form of TAS-20 validated for assessing alexithymia in adolescent populations. Tam & Wong (2012) reported the invariance of the one-factor model of TAS-9-C; yet this resulting model has yet been tested against its original three-factor model of the TAS-20. The objective of this study was to test the goodness-of-fit of both models in three different Chinese samples. Methods Data were obtained from adolescents (n = 1294), healthy adults (n = 196) and adult psychiatric outpatients (n = 243). Confirmatory factor analyses (CFAs) tested the fit of a one-factor model and a correlated model that comprised three correlated first-order factors (including difficulty identifying feelings, difficulty describing feelings and externally-oriented thinking) to the data. The factorial invariance of the TAS-9-C between independent samples was investigated using multigroup CFAs. Results The CFAs results demonstrated a better fit of the correlated three-factor model (CFI > 0.776) over the one-factor model in both adolescent and adult samples (CFI > 0.871). While the correlated model showed evidence of strong factorial invariance among different adolescents from different grade levels, configural invariance was evidenced in the adolescent, healthy adults and adult psychiatric outpatients samples. Conclusions The three meaningful clusters of TAS-9-C mirrored the original factor structure of TAS-20. The use of subscales representing different aspects of alexithymia adds incremental value to the measurement using full scale alone among Chinese across developmental stages and settings. Configural invariance among these samples suggests that growth-related change leads to different developmental trajectories for adult alexithymia. Future research is necessary to ascertain this.
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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 | 0.001 | 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".