Why Do Alexithymic Features Appear to Be Stable?
Bibliographic record
Abstract
BACKGROUND: This 12-month follow-up study investigated the prevalence of alexithymia and its relationship with depression in a sample of the general population from Eastern Finland (n = 1,584). METHODS: Alexithymia was assessed using the 20-item version of the Toronto Alexithymia Scale (TAS) and depression using the 21-item Beck Depression Inventory (BDI). RESULTS: The prevalence of alexithymia in each study phase was similar (baseline: 9.7%; follow-up: 10.1%). Mean values of BDI, TAS-20 and subfactors of the TAS-20 also remained unchanged between the study phases. However, by using the original cutoff points, we found that a proportion of the subjects were in a different TAS-20 category on follow-up than at baseline. The mean values of BDI had not changed in those subjects who had similar alexithymia status in both phases, but increased or decreased in parallel with the change in TAS-20 score among all other subjects. CONCLUSIONS: Our findings indicate that it is important to use a variety of viewpoints when studying changes in alexithymia status. Alexithymia appears to be a stable trait based on the similarity of the mean TAS-20 scores in separate study phases. However, when focusing on the changes in alexithymia status at the individual level, alexithymic features also appear to be state dependent and strongly related to depressive symptoms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".