Health consequences of differences in emotional processing and reactivity following the 1997 earthquake in Central Italy
Bibliographic record
Abstract
The aim of the investigation was to assess the effects of individual differences in emotional processing on health outcomes in persons experiencing the 1997 earthquake in Central Italy. Thirty-nine subjects were examined one month after the event with Toronto Alexithymia Scale (TAS-20), Impact of Event Scale (IES) and a short interview. Interviews were recorded, transcribed and scored for Referential Activity (RA), reflecting 'translation' of non-verbal activation into language. Six months after initial assessment, subjects reported their health in that period. Each subject's report was rated on three health dimensions: Sickness, how physically sick a subject was; distress, how emotionally distressed a subject was; illness behaviour, how often a subject went to see the doctor. Multiple regressions were performed: TAS-20 predicted sickness, IES total predicted distress, while both age and RA predicted illness behaviour. Different parallel levels emerge from the data: a 'here and now' level, linking an intrusive and/or avoidant reaction to the earthquake with subjective distress; a 'deeper', 'structural' level linking a trait difficulty in regulating emotions with the occurrence of actual physical disease. The positive correlation of RA with illness behaviour may be interpreted as an index of general activation of a capacity to seek help.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".