The alexithymia construct: a reading based on Categorical Principal Component Analysis
Bibliographic record
Abstract
En Background: From the 1970s a lot of papers referring to alexithymia have been published. They are mainly focused on clinical subjects, having clear pathologies. In the present work we preferred to consider nonclinical subjects, in particular undergraduate medical students. Aiming at studying the level of alexithymia in the sample, the 20-Item Toronto Alexithymia Scale (TAS-20, Italian version) was used. The objective of this article is (1) to summarize the 20 items, by means of a statistical technique developed to deal with categorical variables, and (2) to interpret the resulting few components. Method: In a single session, 145 undergraduate medical students completed the TAS-20. The answers were analysed by using Categorical Principal Component Analysis (CatPCA). Results: The findings indicate that three was the optimal number of components to reduce the dimensionality of the dataset at hand. Conclusions: The three-component indicator has been interpreted according to analogical, digital, and relationship components. The analogical component identifies a general orientation towards the feelings; it is a kind of theoretical inclination defining the importance of the feelings for the subject; the digital component highlights a greater distinction, a differentiation and also the occurrence of affective ambivalence in the feelings; the relationship component is connected with the relationship, and the other: the subject has to evaluate his feelings again, but in order to test the relationships with the other. All three components identify a general orientation towards the feelings and relationships. It Introduzione: Dagli anni Settanta ad oggi moltissimi sono stati i lavori pubblicati sull’alessitimia, soprattutto in ambito clinico con soggetti che presentano patologie conclamate. Nel presente lavoro abbiamo voluto prendere in considerazione soggetti sani, nello specifico studenti di medicina al terzo anno di corso. Al fine ultimo di studiare il livello di alessitimia presente nel campione, si è utilizzato il TAS-20 (Toronto Alexithymia Scale, versione italiana). In particolare, lo scopo di questo articolo è quello di (1) sintetizzare i 20 item del TAS-20, ricorrendo ad una tecnica statistica creata ad hoc per trattare variabili categoriali, e (2) fornire un’interpretazione per le componenti che ne risultano. Metodo: In una singola sessione, 145 studenti della Facoltà di Medicina hanno compilato il questionario. Le risposte sono state analizzate utilizzando la tecnica statistica dell’Analisi delle Componenti Principali Nonlineare o Categoriale (CatPCA). Risultati: Dalle analisi emerge che il numero ottimale di componenti per ridurre la dimensionalità dei dati a nostra disposizione è pari a tre. Conclusioni: La lettura degli item che maggiormente hanno pesato nelle tre differenti componenti ha dato luogo ad un’interpretazione centrata sulla tematica affettiva. Le tre componenti proposte sono quella analogica (che indica un orientamento generale nei confronti dei sentimenti), quella digitale (che indica la capacità di cogliere i diversi sentimenti e le ambivalenze affettive) e quella relazionale (che indica la capacità di cogliere i propri sentimenti nell’incontro con l’altro). Fr Introduction: Depuis les ans Soixante-dix à aujourd'hui beaucoup de travaux sur l’alexithymie ont été publiés, surtout en domaine clinique avec des sujets qui présentent pathologies acclarées. Dans le présent travail nous avons considéré des sujets noncliniques: étudiants de médecine au troisième an de cours. Pour étudier le niveau d'alexithymie présent dans l’échantillon, on a utilisé le TAS-20 (Toronto Alexithymia Scale) version italienne. Le but de cet article est, en particulier, de: 1) synthétiser les 20 items du TAS-20, en recourant à une technique statistique créé ad hoc pour traiter les variables catégorielles, 2) interpréter les composants qui en résultent. Méthode: Dans une session unique, 145 étudiants de l'Université de Médecine ont complété le questionnaire. Les réponses ont été analysées en utilisant la technique statistique de l'analyse en composantes principales non linéaires. Résultats: L’analyse des données indique que trois est le numéro optimal de composants pour réduire la dimension de l'ensemble de données. Conclusions: La lecture des items qui ont le plus pesé dans les trois composants a donné lieu à une interprétation centrée sur la thématique affective. Les trois propositions composantes sont l’analogique, indiquant une orientation générale vers les sentiments, une sorte d'inclination théorique, la numerique, indiquant la capacité de cueillir les différents sentiments et les ambivalences affectives, et la relationnel, indiquant la capacité a saisir les propres sentiments dans la rencontre avec l’autre.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".