Network approach to items and domains from the Toronto Alexithymia Scale
Bibliographic record
Abstract
The aim of this paper is to explore network structures of the Toronto Alexithymia Scale (TAS) in a large sample of 1925 French-speaking Belgian university students and compare results with previous studies from different samples and tools to identify potential targets for clinical intervention. We estimated network models for the twenty items of the TAS and for its three domains difficulty identifying feelings, difficulty describing feelings and externally-oriented thinking. We explored item connectivity through node predictability (shared variance with other network components). We performed an Exploratory Graph Analysis (EGA) to explore the dimensionality of our dataset and compare results with the original three-factor model; because a different model was proposed, we estimated an additional network structure on the new structure. Items from the TAS connect both within and between domains. The three-domain network identifies difficulty describing feelings as the most connected domain. The EGA reported that three items from externally-oriented thinking form a new domain, distraction. In the new four-domain network, difficulty describing feelings remains the most interconnected domain; however, two negative connections are found.Our findings support the relative importance of identifying and describing feelings as a meaningful target for intervention.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".