Alexithymia, emotions and PTSD; findings from a longitudinal study of refugees
Bibliographic record
Abstract
The objective of the present study was to assess alexithymia by means of the Toronto Alexithymia Scale (TAS-20) and The Emotion Protocol (EP) in a group of refugees. Eighty-six subjects were willing to participate. At last follow-up, 33 non-PTSD and 22 PTSD subjects had complete data. Subjects with PTSD had higher scores on the TAS-20 (F = 4.314, df = 77, p = 0.041), but on the subscale level, this was significant only with regard to Factor I, difficulties identifying feelings (F = 5.316, df = 77, p = 0.024). TAS Factor I and to a lower extent TAS Factor II (difficulties naming feelings) were significantly associated with the self-rated presence of dysphoric affects. At follow-up, an increase in TAS Factor I score was associated with increased prevalence of self-rated symptoms of PTSD, but not depression. Decrease in prolactin was associated with significant increase of TAS Factor I (rho = -0.396, n = 54, p = 0.003). The present study indicates that alexithymia as measured by TAS-20 is indeed associated with symptoms of PTSD. This association is almost exclusively explained by the TAS Factor I subscale and is in turn associated with a high level of self-reported dysphoric affect. The longitudinal inverse correlation with prolactin points to the possibility of an underlying disturbance in serotonergic and/or dopaminergic systems. The results thus indicate that secondary, or post-traumatic, alexithymia is a measure of suppressed or warded-off negative affects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".