The Weight of Words: An analysis of Autobiographical Narratives and Psychopathological Measures in Anorexia Nervosa
Bibliographic record
Abstract
This study aims to explore the relationship between linguistic features of the Referential Process (RP) applied to autobiographical narratives, personality dimensions, and affect regulation capabilities in a group of women diagnosed with restrictive anorexia nervosa (AN). The study included 40 female participants hospitalized with AN during an acute phase, with a mean age of 19.50 (SD = 3.8). Participants completed several assessments, including the Minnesota Multiphasic Personality Inventory 2 (MMPI-2), the Eating Disorder Inventory (EDI-3), the 20-item Toronto Alexithymia Scale (TAS-20), the Emotion Regulation Questionnaire (ERQ), and the Relationship Anecdotes Paradigm Interview (RAP). The RAP interviews were audio-recorded and transcribed for the application of RP Linguistic Measures. The results of the correlation analysis revealed several significant associations among linguistic measures, EDI-3 scale scores, affect regulation measures, and personality dimensions. The linguistic measures indicating higher rationality, abstraction, and cognitive word usage, were associated with higher psychopathological severity in AN. Alexithymia showed significant correlations with the Affect words, supporting the perspective of MCT concerning dissociation of emotional schemas. These findings confirm the relationship between linguistic measures and the severity of the disease. Therefore, autobiographical narratives can be considered not only as diagnostic indicators, but also as variables to verify the efficacy of treatments in patients with AN.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".