Prevalencia de alexitimia en los trastornos de la conducta alimentaria en una muestra clínica de 800 pacientes mexicanas
Bibliographic record
Abstract
Introduction: The inability to identify, express feelings, and not distinguish between emotions and bodily sensations, is known as alexithymia. In 1988, it developed The Toronto Alexithymia Scale (TAS-20), consists of 20 items and three factors: a) difficulty of identifying feelings and differences between feelings and bodily sensations; b) difficulty of describing feelings; and c) externally oriented thinking. It's considered that people with eating disorders have specific deficits in identify and communicate their feelings. Objective: The present study has as purpose to the instrument validation. Methods: It was a cross-sectional study and psychometric character design of a single sample, formed of 435 persons suffering eating disorder (ED), with an age range of 12-68 years, of which 91% were women and 9% were men. To obtain the reliability of the instrument, applies internal consistency test, which resulted in an alpha of 0.89, then applied a factor analysis of principals components with oblimin rotation. Results: According to statistical analysis, were eliminated six items, so the scale finished with 14 items, and to analyze it observed that these items correspond with the two main factors of the original scale. The ED patients present alexithymia. Discussion: The scale satisfies the criteria of validity necessary for use in this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".