Giving words to emotions: the use of linguistic analysis to explore the role of alexithymia in an expressive writing intervention
Bibliographic record
Abstract
Expressive writing techniques are methods focusing on written emotional expression that require people to write about traumatic or difficult experiences, with the objective of promoting an elaboration of these events. The general aim of the study is to investigate the influence of alexithymia, a deficit in emotional regulation processes, on the effects of an expressive writing intervention, analyzing the writing protocols through the use of the Linguistic Inquiry and Word Count (LIWIC) and Referential Process (RP) linguistic measures via IDAAP software. Thirty-five women undergoing an assisted reproductive treatment participated in the study and filled out a sociodemographic questionnaire, the 20-item Toronto Alexithymia Scale. They also underwent three session of writing, following a request that they write about their emotions regarding their current situation. The women enrolled were divided into two groups: low alexithymia and high alexithymia, comprising individuals with a TAS-20 total score lower or higher than the mean, respectively. Analyses within the groups during the three writing sessions revealed that the women with low alexithymia reported a greater number of words expressing affectivity, sadness and future perspective, whereas no significances in the high alexithymia group emerged. Moreover, when analysing differences between the groups, high-alexithymia women reported lower scores in RP indexes and fewer words expressing sadness, future perspectives and we verbal. In conclusion, these preliminary findings may confirm the hypothesis that alexithymia affects the effectiveness of expressive writing through a difficulty in becoming involved in the writing process and a lack of symbolizing processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".