The Development of the Toronto Structured Interview for Alexithymia: Item Selection, Factor Structure, Reliability and Concurrent Validity
Bibliographic record
Abstract
BACKGROUND: Assessments of personality constructs increasingly use self-report and structured interview instruments, which allow for a multimethod measurement approach and decrease specific measurement method bias. The aim of this study was to develop a valid and reliable structured interview for assessing the alexithymia construct. METHODS: Sixty interview questions were written initially, each with a set of scoring criteria and prompts and probes to elicit information assisting in the scoring of the respondents' answers. RESULTS: After pilot testing, the number of questions was reduced to 43, which were administered to 136 community participants and 97 psychiatric outpatients. A series of item and scale analyses further reduced the item pool to 24 items. Principal component analysis and confirmatory factor analysis of these 24 items revealed preliminary evidence of a hierarchical, four-factor structure, with four lower factors nested within two higher-order latent factors. This structural configuration resulted in the Toronto Structured Interview for Alexithymia (TSIA) with two domain scales and four facet scales. The TSIA and its six scales demonstrated acceptable levels of interrater, internal, and retest reliability. The TSIA and its scales correlated modestly but significantly with the 20-item Toronto Alexithymia Scale and its three factor scales, providing some support for the concurrent validity of this interview. CONCLUSION: The TSIA appears to be a promising structured interview for assessing alexithymia.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".