An Examination of the Topology and Measurement of the Alexithymia Construct Using Network Analysis
Bibliographic record
Abstract
There is some ongoing controversy surrounding the definition and measurement of the alexithymia construct. Whereas most researchers describe 4 components comprising the construct (difficulty identifying feelings, difficulty describing feelings, restricted fantasizing, and externally oriented thinking), some include a 5th component, which is defined as "reduced experiencing of emotional feelings." This study examined the topology and measurement of alexithymia using the method of network analysis with data from a heterogeneous multilanguage sample (N = 1,696) that had completed the Bermond-Vorst Alexithymia Questionnaire (BVAQ; Vorst & Bermond, 2001 ). The BVAQ includes an Emotionalizing subscale for assessing the purported 5th component; we compared the network analyses conducted both with and without the Emotionalizing items. The results revealed strong associations between Emotionalizing and Analyzing (externally oriented thinking) items, but Emotionalizing items had almost as many negative as positive connections with items assessing the other components of the construct. A comparison of communities identified by modularity analyses of the 2 networks failed to support emotionalizing as a distinct component of the construct. In addition, network metrics revealed that Fantasizing items were particularly weak within both networks, suggesting that reduced fantasizing might be a peripheral component of the alexithymia construct. Implications for the measurement and treatment of alexithymia are discussed.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".