Assessing alexithymia in forensic settings: Psychometric properties of the 20‐item Toronto Alexithymia Scale among incarcerated adult offenders
Bibliographic record
Abstract
BACKGROUND: Alexithymia is a trait involving difficulty identifying feelings (DIF), difficulty describing feelings (DDF) and externally orientated thinking (EOT). It is a risk factor for criminal behaviour. It is commonly assessed with the Toronto Alexithymia Scale (TAS-20), but the psychometrics of the TAS-20 have not been tested across the range of offender populations, and it has been suggested it might be unsuitable in incarcerated offenders. AIM: To establish the psychometrics of the TAS-20 among incarcerated offenders. METHODS: Factorial validity was examined using confirmatory factor analyses, and the invariance of this factor structure was tested against a published community sample. Reliability coefficients were calculated. RESULTS: One hundred and forty six incarcerated offenders were recruited. The factor structure of the TAS-20 was invariant across the samples. The intended factor structure composed of DIF, DDF and EOT factors performed well overall (with a reverse-scored method factor added), but six EOT items had low factor loadings. The total scale score and DIF and DDF subscales had acceptable reliability, but EOT did not. CONCLUSIONS: Our results suggest that the TAS-20 functions similarly in offender and community samples. Its total scale score, and DIF and DDF subscale scores can be used confidently, but the assessment of externally oriented thinking may not be adequate with this scale alone. In sum, the TAS-20 can facilitate robust assessment of alexithymia in closed criminal justice settings as well as in the wider community.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".