ASSESSING ALEXITHYMIA: A PILOT STUDY USING A COMBINED DESIGN IN A SAMPLE OF LONG TERM DRUG USERS
Bibliographic record
Abstract
Among individuals with the diagnosis of substance-related and addictive disorders (DSM5) the presence of alexithymia is a well-established fact. Out of a sample of 117 middle-aged and older adults (74% aged between 40 and 64) with long-term history of drug dependence who are currently undergoing a treatment plan defined by national drug services, 55 (80% male; Mage=45.10, SD=9.18) were found to have alexithymia as assessed by The Toronto Alexithymia Scale (TAS-20). TAS-20 is a self-report questionnaire that quantitatively measures three components of alexithymia: difficulty in identifying feelings in the self (DIF), difficulty describing feelings (DDF), and externally orientated thinking (EOT). However, in this sample, the expression of the EOT subscale was found to have a minor impact on the global score. This finding is corroborated by previous studies that support TAS-20’s psychometric limitations. To overcome this, a qualitative methodology was introduced to obtain in depth information about the expression of alexithymia in this sub-sample. A semi-structured interview was developed to explore the expression of the subscales mentioned for alexithymia. The interviews were analysed using content analysis. The results underscore a superficial report of emotions and a tendency to describe the external context of the emotion rather than describing the emotional experience itself, which can be interpreted as part of EOT. These results suggest the usefulness of considering a complementary qualitative approach in the study of alexithymia. Clinical interviews that may explore and provide further information on the unique expressions of alexithymia are also be considered, namely for older age categories.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".