Illuminating the theoretical components of alexithymia using bifactor modeling and network analysis.
Bibliographic record
Abstract
Alexithymia is a multifaceted personality construct that reflects deficits in affect awareness (difficulty identifying feelings, DIF; difficulty describing feelings, DDF) and operative thinking (externally oriented thinking, EOT; restricted imaginal processes, IMP), and is associated with several common psychiatric disorders. Over the years, researchers have debated the components that comprise the construct with some suggesting that IMP and EOT may reflect constructs somewhat distinct from alexithymia. In this investigation, we attempt to clarify the components and their interrelationships using a large heterogeneous multilanguage sample (N = 839), and an interview-based assessment of alexithymia (Toronto Structured Interview for Alexithymia; TSIA). To this end, we used 2 distinctly different but complementary methods, bifactor modeling and network analysis. Results of the confirmatory bifactor model and related reliability estimates supported a strong general factor of alexithymia; however, the majority of reliable variance for IMP was independent of this general factor. In contrast, network analysis results were based on a network comprised of only substantive partial correlations among TSIA items. Modularity analysis revealed 3 communities of items, where DIF and DDF formed 1 community, and EOT and IMP formed separate communities. Network metrics supported that the majority of central items resided in the DIF/DDF community and that IMP items were connected to the network primarily through EOT. Taken together, results suggest that IMP, at least as measured by the TSIA, may not be as salient a component of the alexithymia construct as are the DIF, DDF, and EOT components. (PsycINFO Database Record
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".