Psychometric Properties of the Alexithymia Questionnaire for Children in a Peruvian Sample of Adolescents
Bibliographic record
Abstract
The concept of alexithymia refers to impairments in the ability to identify and communicate feelings. Alexithymia has repeatedly been linked to attachment impairments and different types of symptomatology, in particular, depression and somatic complaints. Very few studies have focused on children or adolescents when addressing this construct. Additionally, to date, there is no self-report questionnaire of alexithymia for such groups in the Spanish language. The main objectives of this study were therefore, (a) to translate and adapt the Alexithymia Questionnaire for Children to Spanish; (b) to assess the factor structure of the adapted questionnaire; and (c) to describe its reliability and validity, in a sample of N = 265 Peruvian adolescents aged 11-18 years. Internal consistency was acceptable for the DIF subscale (α = .74), and low for the DDF and EOT subscales (α = .55, and α = .47 respectively). A composite scale based on previous studies that merges DIF and DDF into one scale had an α = .75. Regarding the factor structure, a two-factor solution showed to have the best fit with the data (RMSEA = .05, SMRM = .04, CFI = .94). Convergent validity analyses indicated significant associations between alexithymia and attachment measurements (that ranged from r = - .15, p < .05, to r = .31, p < .05), somatic complaints (r = .38, p < .05, to r = .41, p < .05), and both internalizing and externalizing symptoms (r = .37, p < .05, to r = .46, p < .05). Future assessment and modifications are recommended for the EOT scale.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".