Autobiographical narratives in relation to affect regulation in an Italian sample of patients in an acute phase of Anorexia Nervosa
Bibliographic record
Abstract
Abstract Purpose: This research aims to analyze the relationship between emotional regulation and the symbolic process in autobiographical narratives in a group of individuals diagnosed with restrictive Anorexia Nervosa (AN), compared to a non-clinical group. The study is framed within Multiple Code Theory MCT (Bucci, 1997; 2021) which considers mind-body integration. The purposes of this study are to investigate whether AN will show greater alexithymia and emotional dysregulation than the non-clinical group; and whether the specific linguistic and symbolic features, such as somatic-sensory words, affect words, and difficulty in symbolizing process will predict the AN group.Methods: Twenty-nine female participants hospitalized with AN in an acute phase (mean age 19.8 ±4.1) and thirty-six non-clinical female participants (mean age 21 ±2.4) were selected through snow-ball sampling. The participants completed the Toronto Alexithymia Scale (TAS-20), the Profile of Mood of State (POMS), the Emotion Regulation Questionnaire (ERQ), and a Relationship Anecdotes Paradigm Interview (RAP). The Referential Process (RP) Linguistic Measures has been applied to transcribed interviews. A T-test for paired samples and logistic binary regression has been performed.Results: AN presented a significantly higher emotional dysregulation through the ERQ, TAS20 and POMS measures. Specifically, AN showed higher ER expression/suppression strategies, fewer functional cognitive strategies, higher alexithymia, and higher mood dysregulation. Specific linguistic features such as sensory-somatic, affect word, and difficulty in RP symbolizing, predict the AN group (R2 = .349; χ2 = 27,929; df = 3; p = .001).Conclusions: Emotional dysregulation is connected to AN symptoms and autobiographical narratives. The results can help a clinical assessment phase showing specific linguistic features in AN patients.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".